



"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 					a	(  lowerCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				Union[str, Any]     						=					RoFormerTokenizer
  UpperCamelCase	:				List[Any]     						=					RoFormerTokenizerFast
  UpperCamelCase	:				Tuple     						=					True
  UpperCamelCase	:				Any     						=					True



  def 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''simple docstring'''




     super().setUp()



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		**lowerCAmelCase :					Optional[int]		)     ->					Tuple:


     '''simple docstring'''




     return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""       ,		**_lowerCamelCase		)



  def 	lowerCamelCase__       (						self :					List[Any]       ,		**lowerCAmelCase :					int		)     ->					Tuple:


     '''simple docstring'''




     return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""       ,		**_lowerCamelCase		)



  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			="""永和服装饰品有限公司,今天天气非常好"""
     SCREAMING_SNAKE_CASE_: List[str]     			="""永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
     return input_text, output_text



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=self.get_tokenizer()
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.get_chinese_input_output_texts()
     SCREAMING_SNAKE_CASE_: Dict     			=tokenizer.tokenize(_lowerCamelCase		)

     self.assertListEqual(_lowerCamelCase       ,		output_text.split()		)

     SCREAMING_SNAKE_CASE_: Dict     			=tokens + [tokenizer.unk_token]
     SCREAMING_SNAKE_CASE_: int     			=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase		)       ,		_lowerCamelCase		)



  def 	lowerCamelCase__       (						self :					Dict		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=self.get_chinese_input_output_texts()
     SCREAMING_SNAKE_CASE_: Optional[int]     			=tokenizer.tokenize(_lowerCamelCase		)
     self.assertListEqual(_lowerCamelCase       ,		output_text.split()		)
     SCREAMING_SNAKE_CASE_: int     			=tokens + [tokenizer.unk_token]
     SCREAMING_SNAKE_CASE_: Any     			=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase		)       ,		_lowerCamelCase		)



  def 	lowerCamelCase__       (						self :					str		)     ->					Union[str, Any]:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     pass





  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     pass








"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase     =			logging.get_logger("""transformers.models.speecht5""")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[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 ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

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

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""output_conv.1.weight_g"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint["""output_conv.1.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=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__":
   _UpperCAmelCase     =			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     =			parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )



