


'''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    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Union[str, Any]:
    hf_model.apply_weight_norm()

    UpperCAmelCase__     :						Tuple						=							checkpoint['''input_conv.weight_g''']
    UpperCAmelCase__     :						Any						=							checkpoint['''input_conv.weight_v''']
    UpperCAmelCase__     :						Union[str, Any]						=							checkpoint['''input_conv.bias''']

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

            UpperCAmelCase__     :						Tuple						=							checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
            UpperCAmelCase__     :						Tuple						=							checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
            UpperCAmelCase__     :						List[Any]						=							checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

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

    hf_model.remove_weight_norm()





@torch.no_grad()
def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None    ,   )       ->      List[str]:
    if config_path is not None:
        UpperCAmelCase__     :						Tuple						=							SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__		)
    else:
        UpperCAmelCase__     :						Union[str, Any]						=							SpeechTaHifiGanConfig()

    UpperCAmelCase__     :						Optional[int]						=							SpeechTaHifiGan(lowerCAmelCase__		)

    UpperCAmelCase__     :						Any						=							torch.load(lowerCAmelCase__		)
    load_weights(orig_checkpoint['''model''']['''generator''']    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)

    UpperCAmelCase__     :						Optional[int]						=							np.load(lowerCAmelCase__		)
    UpperCAmelCase__     :						str						=							stats[0].reshape(-1		)
    UpperCAmelCase__     :						Optional[int]						=							stats[1].reshape(-1		)
    UpperCAmelCase__     :						Tuple						=							torch.from_numpy(lowerCAmelCase__		).float()
    UpperCAmelCase__     :						Union[str, 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__":
  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,
  )




'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =CodeGenTokenizer
       UpperCAmelCase		 =CodeGenTokenizerFast
       UpperCAmelCase		 =True
       UpperCAmelCase		 ={"add_prefix_space": True}
       UpperCAmelCase		 =False






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          _UpperCAmelCase  :  Optional[Any]				       =[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
              '<|endoftext|>',
          ]
          _UpperCAmelCase  :  Dict				       =dict(zip(snake_case    ,				range(len(snake_case))))
          _UpperCAmelCase  :  List[Any]				       =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          _UpperCAmelCase  :  List[Any]				       ={'unk_token': '<unk>'}

          _UpperCAmelCase  :  List[Any]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file'])
          _UpperCAmelCase  :  List[str]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write(json.dumps(snake_case) + '\n')
          with open(self.merges_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write('\n'.join(snake_case))






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    str:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'
          _UpperCAmelCase  :  int				       ='lower newer'
          return input_text, output_text






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =CodeGenTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map)
          _UpperCAmelCase  :  str				       ='lower newer'
          _UpperCAmelCase  :  Union[str, Any]				       =['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
          _UpperCAmelCase  :  Any				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          _UpperCAmelCase  :  Any				       =tokens + [tokenizer.unk_token]
          _UpperCAmelCase  :  List[str]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






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




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase  :  Tuple				       =self.get_tokenizer()
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'

          # Testing tokenization
          _UpperCAmelCase  :  List[str]				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids without special tokens
          _UpperCAmelCase  :  str				       =tokenizer.encode(snake_case    ,				add_special_tokens=snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids with special tokens
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer.encode(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  int				       =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing the unknown token
          _UpperCAmelCase  :  List[str]				       =tokens + [rust_tokenizer.unk_token]
          _UpperCAmelCase  :  Union[str, Any]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Any:




          '''simple docstring'''
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass






       def      lowerCAmelCase						(     self    ,				snake_case=1_5)		->    Optional[int]:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase  :  Optional[Any]				       =self.rust_tokenizer_class.from_pretrained(snake_case    ,				**snake_case)

                # Simple input
                _UpperCAmelCase  :  List[str]				       ='This is a simple input'
                _UpperCAmelCase  :  Optional[Any]				       =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase  :  int				       =('This is a simple input', 'This is a pair')
                _UpperCAmelCase  :  Union[str, Any]				       =[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='<pad>')

          # Simple input
          _UpperCAmelCase  :  List[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input looooooooong', 'This is a simple input']
          _UpperCAmelCase  :  List[str]				       =('This is a simple input', 'This is a pair')
          _UpperCAmelCase  :  Optional[int]				       =[
              ('This is a simple input loooooong', 'This is a simple input'),
              ('This is a simple pair loooooong', 'This is a simple pair'),
          ]

          _UpperCAmelCase  :  List[Any]				       =tokenizer.pad_token_id

          _UpperCAmelCase  :  Union[str, Any]				       =tokenizer(snake_case    ,				padding='max_length'    ,				max_length=3_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')
          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(*snake_case    ,				padding='max_length'    ,				max_length=6_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')

          # s
          # test single string max_length padding
          self.assertEqual(out_s['input_ids'].shape[-1]    ,				3_0)
          self.assertTrue(pad_token_id in out_s['input_ids'])
          self.assertTrue(0 in out_s['attention_mask'])

          # s2
          # test automatic padding
          self.assertEqual(out_sa['input_ids'].shape[-1]    ,				3_3)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['input_ids'][0])
          self.assertFalse(0 in out_sa['attention_mask'][0])
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['input_ids'][1])
          self.assertTrue(0 in out_sa['attention_mask'][1])

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['input_ids'].shape[-1]    ,				6_0)
          self.assertTrue(pad_token_id in out_p['input_ids'])
          self.assertTrue(0 in out_p['attention_mask'])

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['input_ids'].shape[-1]    ,				5_2)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['input_ids'][0])
          self.assertFalse(0 in out_pa['attention_mask'][0])
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['input_ids'][1])
          self.assertTrue(0 in out_pa['attention_mask'][1])






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ='$$$'
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				bos_token=snake_case    ,				add_bos_token=snake_case)

          _UpperCAmelCase  :  Optional[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input 1', 'This is a simple input 2']

          _UpperCAmelCase  :  int				       =tokenizer.bos_token_id

          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer(snake_case)

          self.assertEqual(out_s.input_ids[0]    ,				snake_case)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.decode(out_s.input_ids)
          _UpperCAmelCase  :  Any				       =tokenizer.batch_decode(out_sa.input_ids)

          self.assertEqual(decode_s.split()[0]    ,				snake_case)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

          _UpperCAmelCase  :  Optional[Any]				       ='\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
          _UpperCAmelCase  :  Tuple				       ='\nif len_a > len_b:      result = a\nelse:      result = b'

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.encode(snake_case)
          _UpperCAmelCase  :  List[str]				       =['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
          _UpperCAmelCase  :  Dict				       =tokenizer.decode(snake_case    ,				truncate_before_pattern=snake_case)
          self.assertEqual(snake_case    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          pass

