



'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__:   List[str]								=			logging.get_logger("transformers.models.speecht5")


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   List[Any]:
							hf_model.apply_weight_norm()

							UpperCAmelCase :       List[Any] 							=		checkpoint['''input_conv.weight_g''']
							UpperCAmelCase :       Optional[int] 							=		checkpoint['''input_conv.weight_v''']
							UpperCAmelCase :       Any 							=		checkpoint['''input_conv.bias''']

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

																					UpperCAmelCase :       List[Any] 							=		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					UpperCAmelCase :       Union[str, Any] 							=		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					UpperCAmelCase :       str 							=		checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

							UpperCAmelCase :       Tuple 							=		checkpoint['''output_conv.1.weight_g''']
							UpperCAmelCase :       Union[str, Any] 							=		checkpoint['''output_conv.1.weight_v''']
							UpperCAmelCase :       Dict 							=		checkpoint['''output_conv.1.bias''']

							hf_model.remove_weight_norm()


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Union[str, Any]=None   ,      _lowerCAmelCase  :						List[str]=None   ,      )      ->   List[Any]:
							if config_path is not None:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig()

							UpperCAmelCase :       Dict 							=		SpeechTaHifiGan(_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		torch.load(_lowerCAmelCase  )
							load_weights(orig_checkpoint['''model''']['''generator''']   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Any 							=		np.load(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		stats[0].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		stats[1].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		torch.from_numpy(_lowerCAmelCase  ).float()
							UpperCAmelCase :       List[str] 							=		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__":
						UpperCamelCase__:   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."
						)

						UpperCamelCase__:   Optional[int]								=			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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :				str									=  logging.get_logger(__name__)

a  :				str									=  {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"big_bird"


				def __init__(		self    :					Union[str, Any]       ,				lowercase_    :					List[Any]=5_0358       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					Dict=12       ,				lowercase_    :					str=12       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Any="gelu_new"       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					List[Any]=4096       ,				lowercase_    :					List[Any]=2       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					Optional[int]=1e-12       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Tuple=0       ,				lowercase_    :					str=1       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Optional[Any]=66       ,				lowercase_    :					Optional[int]="block_sparse"       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[str]=False       ,				lowercase_    :					Any=64       ,				lowercase_    :					Tuple=3       ,				lowercase_    :					Tuple=None       ,				**lowercase_    :					Tuple       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				max_position_embeddings
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				type_vocab_size
									snake_case_									=				layer_norm_eps
									snake_case_									=				use_cache

									snake_case_									=				rescale_embeddings
									snake_case_									=				attention_type
									snake_case_									=				use_bias
									snake_case_									=				block_size
									snake_case_									=				num_random_blocks
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):


				@property
				def   A_ (		self    :					str					):
									if self.task == "multiple-choice":
														snake_case_									=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_									=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]					)

