




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__				:      Any	   = logging.get_logger("transformers.models.speecht5")

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:
					hf_model.apply_weight_norm()

					a     =				checkpoint["""input_conv.weight_g"""]
					a     =				checkpoint["""input_conv.weight_v"""]
					a     =				checkpoint["""input_conv.bias"""]

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

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

					a     =				checkpoint["""output_conv.1.weight_g"""]
					a     =				checkpoint["""output_conv.1.weight_v"""]
					a     =				checkpoint["""output_conv.1.bias"""]

					hf_model.remove_weight_norm()

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=None		, )      ->					List[Any]:





					if config_path is not None:
										a     =				SpeechTaHifiGanConfig.from_pretrained(lowercase__)
					else:
										a     =				SpeechTaHifiGanConfig()

					a     =				SpeechTaHifiGan(lowercase__)

					a     =				torch.load(lowercase__)
					load_weights(orig_checkpoint["model"]["generator"]		, lowercase__		, lowercase__)

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

					model.save_pretrained(lowercase__)

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


if __name__ == "__main__":
			lowercase__				:      Optional[int]	   = 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."
			)

			lowercase__				:      List[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,
			)







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 ..auto import CONFIG_MAPPING


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)

lowercase__				:      str	   = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''simple docstring'''



          if backbone_config is not None and use_timm_backbone:
               raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."			)

          if not use_timm_backbone:
               if backbone_config is None:
                    logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

          a     =				use_timm_backbone
          a     =				backbone_config
          a     =				num_channels
          a     =				num_queries
          a     =				d_model
          a     =				encoder_ffn_dim
          a     =				encoder_layers
          a     =				encoder_attention_heads
          a     =				decoder_ffn_dim
          a     =				decoder_layers
          a     =				decoder_attention_heads
          a     =				dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				activation_function
          a     =				init_std
          a     =				init_xavier_std
          a     =				encoder_layerdrop
          a     =				decoder_layerdrop
          a     =				encoder_layers
          a     =				auxiliary_loss
          a     =				position_embedding_type
          a     =				backbone
          a     =				use_pretrained_backbone
          a     =				dilation
          # Hungarian matcher
          a     =				class_cost
          a     =				bbox_cost
          a     =				giou_cost
          # Loss coefficients
          a     =				mask_loss_coefficient
          a     =				dice_loss_coefficient
          a     =				bbox_loss_coefficient
          a     =				giou_loss_coefficient
          a     =				eos_coefficient
          super().__init__(is_encoder_decoder=A  ,		**A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

