






'''simple docstring'''

from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}






class     a_						(      a_    ):
		lowercase							=				"""informer"""
		lowercase							=				{
		    """hidden_size""": """d_model""",
		    """num_attention_heads""": """encoder_attention_heads""",
		    """num_hidden_layers""": """encoder_layers""",
		}

		def __init__(				self      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "student_t"      ,		_SCREAMING_SNAKE_CASE = "nll"      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "mean"      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 64      ,		_SCREAMING_SNAKE_CASE = 32      ,		_SCREAMING_SNAKE_CASE = 32      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = "gelu"      ,		_SCREAMING_SNAKE_CASE = 0.0_5      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 100      ,		_SCREAMING_SNAKE_CASE = 0.0_2      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE = "prob"      ,		_SCREAMING_SNAKE_CASE = 5      ,		_SCREAMING_SNAKE_CASE = True      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[int]:



								"""simple docstring"""






								UpperCamelCase      = prediction_length
								UpperCamelCase      = context_length or prediction_length
								UpperCamelCase      = distribution_output
								UpperCamelCase      = loss
								UpperCamelCase      = input_size
								UpperCamelCase      = num_time_features
								UpperCamelCase      = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
								UpperCamelCase      = scaling
								UpperCamelCase      = num_dynamic_real_features
								UpperCamelCase      = num_static_real_features
								UpperCamelCase      = num_static_categorical_features

								# set cardinality
								if cardinality and num_static_categorical_features > 0:
														if len(lowercase_				) != num_static_categorical_features:
																				raise ValueError(
																				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
														UpperCamelCase      = cardinality
								else:
														UpperCamelCase      = [0]

								# set embedding_dimension
								if embedding_dimension and num_static_categorical_features > 0:
														if len(lowercase_				) != num_static_categorical_features:
																				raise ValueError(
																				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
														UpperCamelCase      = embedding_dimension
								else:
														UpperCamelCase      = [min(50      ,		(cat + 1) // 2				) for cat in self.cardinality]

								UpperCamelCase      = num_parallel_samples

								# Transformer architecture configuration
								UpperCamelCase      = input_size * len(self.lags_sequence				) + self._number_of_features
								UpperCamelCase      = d_model
								UpperCamelCase      = encoder_attention_heads
								UpperCamelCase      = decoder_attention_heads
								UpperCamelCase      = encoder_ffn_dim
								UpperCamelCase      = decoder_ffn_dim
								UpperCamelCase      = encoder_layers
								UpperCamelCase      = decoder_layers

								UpperCamelCase      = dropout
								UpperCamelCase      = attention_dropout
								UpperCamelCase      = activation_dropout
								UpperCamelCase      = encoder_layerdrop
								UpperCamelCase      = decoder_layerdrop

								UpperCamelCase      = activation_function
								UpperCamelCase      = init_std

								UpperCamelCase      = use_cache

								# Informer
								UpperCamelCase      = attention_type
								UpperCamelCase      = sampling_factor
								UpperCamelCase      = distil

								super().__init__(is_encoder_decoder=lowercase_      ,		**lowercase_				)


		@property
		def   A__						(				self				)  ->				Union[str, Any]:



								"""simple docstring"""






								return (
								    sum(self.embedding_dimension				)
								    + self.num_dynamic_real_features
								    + self.num_time_features
								    + self.num_static_real_features
								    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
								)








'''simple docstring'''

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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

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

      model.save_pretrained(__UpperCamelCase   )

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


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,
     )
