

'''simple docstring'''

import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @parameterized.expand([(None,), ('foo.json',)]	)
     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					int	)						->     Dict:
      UpperCAmelCase	:     Dict						 =			GenerationConfig(
          do_sample=lowercase_					,      temperature=0.7					,      length_penalty=1.0					,      bad_words_ids=[[1, 2, 3], [4, 5]]					,      )
      with tempfile.TemporaryDirectory() as tmp_dir:
       config.save_pretrained(lowercase_					,      config_name=lowercase_	)
       UpperCAmelCase	:     Optional[int]						 =			GenerationConfig.from_pretrained(lowercase_					,      config_name=lowercase_	)

      # Checks parameters that were specified
      self.assertEqual(loaded_config.do_sample					,      lowercase_	)
      self.assertEqual(loaded_config.temperature					,      0.7	)
      self.assertEqual(loaded_config.length_penalty					,      1.0	)
      self.assertEqual(loaded_config.bad_words_ids					,      [[1, 2, 3], [4, 5]]	)

      # Checks parameters that were not specified (defaults)
      self.assertEqual(loaded_config.top_k					,      50	)
      self.assertEqual(loaded_config.max_length					,      20	)
      self.assertEqual(loaded_config.max_time					,      lowercase_	)


     def        UpperCAmelCase_	(		self			:					Any	)						->     Dict:
      UpperCAmelCase	:     Tuple						 =			AutoConfig.from_pretrained('gpt2'	)
      UpperCAmelCase	:     Any						 =			GenerationConfig.from_model_config(lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			GenerationConfig()

      # The generation config has loaded a few non-default parameters from the model config
      self.assertNotEqual(lowercase_					,      lowercase_	)

      # One of those parameters is eos_token_id -- check if it matches
      self.assertNotEqual(generation_config_from_model.eos_token_id					,      default_generation_config.eos_token_id	)
      self.assertEqual(generation_config_from_model.eos_token_id					,      model_config.eos_token_id	)


     def        UpperCAmelCase_	(		self			:					int	)						->     Any:
      UpperCAmelCase	:     Optional[Any]						 =			GenerationConfig()
      UpperCAmelCase	:     Optional[Any]						 =			{
          'max_new_tokens': 1_024,
          'foo': 'bar',
      }
      UpperCAmelCase	:     Tuple						 =			copy.deepcopy(lowercase_	)
      UpperCAmelCase	:     int						 =			generation_config.update(**lowercase_	)

      # update_kwargs was not modified (no side effects)
      self.assertEqual(lowercase_					,      lowercase_	)

      # update_kwargs was used to update the config on valid attributes
      self.assertEqual(generation_config.max_new_tokens					,      1_024	)

      # `.update()` returns a dictionary of unused kwargs
      self.assertEqual(lowercase_					,      {'foo': 'bar'}	)


     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      UpperCAmelCase	:     List[Any]						 =			GenerationConfig()
      UpperCAmelCase	:     str						 =			'bar'

      with tempfile.TemporaryDirectory('test-generation-config'	) as tmp_dir:
       generation_config.save_pretrained(lowercase_	)

       UpperCAmelCase	:     str						 =			GenerationConfig.from_pretrained(lowercase_	)
      # update_kwargs was used to update the config on valid attributes
      self.assertEqual(new_config.foo					,      'bar'	)

      UpperCAmelCase	:     Tuple						 =			GenerationConfig.from_model_config(lowercase_	)
      assert not hasattr(lowercase_					,      'foo'	)  # no new kwargs should be initialized if from config




     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      UpperCAmelCase	:     Dict						 =			GenerationConfig()
      self.assertEqual(default_config.temperature					,      1.0	)
      self.assertEqual(default_config.do_sample					,      lowercase_	)
      self.assertEqual(default_config.num_beams					,      1	)

      UpperCAmelCase	:     Any						 =			GenerationConfig(
          do_sample=lowercase_					,      temperature=0.7					,      length_penalty=1.0					,      bad_words_ids=[[1, 2, 3], [4, 5]]					,      )
      self.assertEqual(config.temperature					,      0.7	)
      self.assertEqual(config.do_sample					,      lowercase_	)
      self.assertEqual(config.num_beams					,      1	)

      with tempfile.TemporaryDirectory() as tmp_dir:
       config.save_pretrained(lowercase_	)
       UpperCAmelCase	:     Optional[Any]						 =			GenerationConfig.from_pretrained(lowercase_					,      temperature=1.0	)

      self.assertEqual(loaded_config.temperature					,      1.0	)
      self.assertEqual(loaded_config.do_sample					,      lowercase_	)
      self.assertEqual(loaded_config.num_beams					,      1	)  # default value




@is_staging_test
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @classmethod
     def        UpperCAmelCase_	(		cls			:					Dict	)						->     Union[str, Any]:
      UpperCAmelCase	:     int						 =			TOKEN
      HfFolder.save_token(lowercase_	)


     @classmethod
     def        UpperCAmelCase_	(		cls			:					int	)						->     int:
      try:
       delete_repo(token=cls._token					,      repo_id='test-generation-config'	)
      except HTTPError:
       pass

      try:
       delete_repo(token=cls._token					,      repo_id='valid_org/test-generation-config-org'	)
      except HTTPError:
       pass


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Any:
      UpperCAmelCase	:     Optional[int]						 =			GenerationConfig(
          do_sample=lowercase_					,      temperature=0.7					,      length_penalty=1.0					,      )
      config.push_to_hub('test-generation-config'					,      use_auth_token=self._token	)

      UpperCAmelCase	:     Optional[int]						 =			GenerationConfig.from_pretrained(f"""{USER}/test-generation-config"""	)
      for k, v in config.to_dict().items():
       if k != "transformers_version":
        self.assertEqual(lowercase_					,      getattr(lowercase_					,      lowercase_	)	)

        # Reset repo
      delete_repo(token=self._token					,      repo_id='test-generation-config'	)

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
       config.save_pretrained(
           lowercase_					,      repo_id='test-generation-config'					,      push_to_hub=lowercase_					,      use_auth_token=self._token	)

      UpperCAmelCase	:     List[Any]						 =			GenerationConfig.from_pretrained(f"""{USER}/test-generation-config"""	)
      for k, v in config.to_dict().items():
       if k != "transformers_version":
        self.assertEqual(lowercase_					,      getattr(lowercase_					,      lowercase_	)	)




     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Union[str, Any]:
      UpperCAmelCase	:     Tuple						 =			GenerationConfig(
          do_sample=lowercase_					,      temperature=0.7					,      length_penalty=1.0					,      )
      config.push_to_hub('valid_org/test-generation-config-org'					,      use_auth_token=self._token	)

      UpperCAmelCase	:     List[Any]						 =			GenerationConfig.from_pretrained('valid_org/test-generation-config-org'	)
      for k, v in config.to_dict().items():
       if k != "transformers_version":
        self.assertEqual(lowercase_					,      getattr(lowercase_					,      lowercase_	)	)

        # Reset repo
      delete_repo(token=self._token					,      repo_id='valid_org/test-generation-config-org'	)

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
       config.save_pretrained(
           lowercase_					,      repo_id='valid_org/test-generation-config-org'					,      push_to_hub=lowercase_					,      use_auth_token=self._token	)

      UpperCAmelCase	:     Any						 =			GenerationConfig.from_pretrained('valid_org/test-generation-config-org'	)
      for k, v in config.to_dict().items():
       if k != "transformers_version":
        self.assertEqual(lowercase_					,      getattr(lowercase_					,      lowercase_	)	)







'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

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

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			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__":
 lowercase__         =      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__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )




