






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__				     =			logging.get_logger('''transformers.models.speecht5''')

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Union[str, Any]				):
			hf_model.apply_weight_norm()

			__a						:  int									=     checkpoint['input_conv.weight_g']
			__a						:  Optional[Any]									=     checkpoint['input_conv.weight_v']
			__a						:  str									=     checkpoint['input_conv.bias']

			for i in range(len(config.upsample_rates				)				):
						__a						:  Tuple									=     checkpoint[f'''upsamples.{i}.1.weight_g''']
						__a						:  Tuple									=     checkpoint[f'''upsamples.{i}.1.weight_v''']
						__a						:  Optional[Any]									=     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				)				):
									__a						:  Optional[Any]									=     checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
									__a						:  Tuple									=     checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
									__a						:  int									=     checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

									__a						:  List[str]									=     checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
									__a						:  str									=     checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
									__a						:  Optional[Any]									=     checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

			__a						:  List[str]									=     checkpoint['output_conv.1.weight_g']
			__a						:  int									=     checkpoint['output_conv.1.weight_v']
			__a						:  List[str]									=     checkpoint['output_conv.1.bias']

			hf_model.remove_weight_norm()

@torch.no_grad()
def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Dict=None   ,  lowerCamelCase_       :       Optional[Any]=None   ,  ):
			if config_path is not None:
						__a						:  List[Any]									=     SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase_				)
			else:
						__a						:  Optional[Any]									=     SpeechTaHifiGanConfig()

			__a						:  Tuple									=     SpeechTaHifiGan(lowerCamelCase_				)

			__a						:  Optional[int]									=     torch.load(lowerCamelCase_				)
			load_weights(orig_checkpoint['model']['generator']   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			__a						:  List[Any]									=     np.load(lowerCamelCase_				)
			__a						:  Optional[Any]									=     stats[0].reshape(-1				)
			__a						:  str									=     stats[1].reshape(-1				)
			__a						:  Union[str, Any]									=     torch.from_numpy(lowerCamelCase_				).float()
			__a						:  Optional[Any]									=     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__":
							SCREAMING_SNAKE_CASE__				     =			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.'''
							)

							SCREAMING_SNAKE_CASE__				     =			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 packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


