


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase  :		Tuple		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Union[str, Any]		     =	{
    """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  lowerCAmelCase				(				_a							):
	_lowerCamelCase   :					Optional[int]			      = """big_bird"""



	def __init__(					self ,					snake_case__=5_0358 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu_new" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=4096 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=True ,					snake_case__=0 ,					snake_case__=1 ,					snake_case__=2 ,					snake_case__=66 ,					snake_case__="block_sparse" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=64 ,					snake_case__=3 ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(
		    pad_token_id=snake_case_ ,					bos_token_id=snake_case_ ,					eos_token_id=snake_case_ ,					sep_token_id=snake_case_ ,					**snake_case_ ,					)

		lowerCAmelCase					:    Optional[Any]      							= vocab_size
		lowerCAmelCase					:    List[Any]      							= max_position_embeddings
		lowerCAmelCase					:    Union[str, Any]      							= hidden_size
		lowerCAmelCase					:    Dict      							= num_hidden_layers
		lowerCAmelCase					:    Dict      							= num_attention_heads
		lowerCAmelCase					:    List[str]      							= intermediate_size
		lowerCAmelCase					:    List[str]      							= hidden_act
		lowerCAmelCase					:    Tuple      							= hidden_dropout_prob
		lowerCAmelCase					:    Any      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Any      							= initializer_range
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    Optional[int]      							= layer_norm_eps
		lowerCAmelCase					:    List[Any]      							= use_cache

		lowerCAmelCase					:    str      							= rescale_embeddings
		lowerCAmelCase					:    Union[str, Any]      							= attention_type
		lowerCAmelCase					:    Dict      							= use_bias
		lowerCAmelCase					:    Optional[int]      							= block_size
		lowerCAmelCase					:    Any      							= num_random_blocks
		lowerCAmelCase					:    Union[str, Any]      							= classifier_dropout




class  lowerCAmelCase				(				_a							):



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




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

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

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	lowerCAmelCase					:    List[Any]      							= checkpoint['output_conv.1.weight_g']
	lowerCAmelCase					:    List[str]      							= checkpoint['output_conv.1.weight_v']
	lowerCAmelCase					:    Optional[Any]      							= checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

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

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    int      							= torch.from_numpy(_A				).float()

	model.save_pretrained(_A				)

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


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  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
