





'''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	(	a    : int					,   a    : int					,   a    : Tuple     ) ->List[Any]:
	hf_model.apply_weight_norm()

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

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

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

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

	hf_model.remove_weight_norm()

@torch.no_grad()
def    __UpperCamelCase	(	a    : List[Any]					,   a    : Union[str, Any]					,   a    : int					,   a    : str=None					,   a    : Dict=None					,   ) ->Any:

	if config_path is not None:
		snake_case				=			SpeechTaHifiGanConfig.from_pretrained(snake_case__     )
	else:
		snake_case				=			SpeechTaHifiGanConfig()

	snake_case				=			SpeechTaHifiGan(snake_case__     )

	snake_case				=			torch.load(snake_case__     )
	load_weights(orig_checkpoint['''model''']['''generator''']					,   snake_case__					,   snake_case__     )

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

	model.save_pretrained(snake_case__     )

	if repo_id:
		print('''Pushing to the hub...'''     )
		model.push_to_hub(snake_case__     )


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,
			)








'''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 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					CodeGenTokenizer
     _UpperCAmelCase										=					CodeGenTokenizerFast
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					{'''add_prefix_space''': True}
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	Tuple:
      super().setUp()

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

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







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:
      kwargs.update(self.special_tokens_map    )
      return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:
      kwargs.update(self.special_tokens_map    )
      return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
      snake_case				=			'''lower newer'''
      snake_case				=			'''lower newer'''
      return input_text, output_text







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			CodeGenTokenizer(self.vocab_file    ,   self.merges_file    ,   **self.special_tokens_map    )
      snake_case				=			'''lower newer'''
      snake_case				=			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      snake_case				=			tokenizer.tokenize(A__    ,   add_prefix_space=A__    )
      self.assertListEqual(A__    ,   A__    )

      snake_case				=			tokens + [tokenizer.unk_token]
      snake_case				=			[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A__    )    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      if not self.test_rust_tokenizer:
       return

      snake_case				=			self.get_tokenizer()
      snake_case				=			self.get_rust_tokenizer(add_prefix_space=A__    )

      snake_case				=			'''lower newer'''

      # Testing tokenization
      snake_case				=			tokenizer.tokenize(A__    ,   add_prefix_space=A__    )
      snake_case				=			rust_tokenizer.tokenize(A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing conversion to ids without special tokens
      snake_case				=			tokenizer.encode(A__    ,   add_special_tokens=A__    ,   add_prefix_space=A__    )
      snake_case				=			rust_tokenizer.encode(A__    ,   add_special_tokens=A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing conversion to ids with special tokens
      snake_case				=			self.get_rust_tokenizer(add_prefix_space=A__    )
      snake_case				=			tokenizer.encode(A__    ,   add_prefix_space=A__    )
      snake_case				=			rust_tokenizer.encode(A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing the unknown token
      snake_case				=			tokens + [rust_tokenizer.unk_token]
      snake_case				=			[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__    )    ,   A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	List[str]:
      # 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     UpperCamelCase			( self    ,   A__=15    ) ->	Tuple:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
        snake_case				=			self.rust_tokenizer_class.from_pretrained(A__    ,   **A__    )

        # Simple input
        snake_case				=			'''This is a simple input'''
        snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']
        snake_case				=			('''This is a simple input''', '''This is a pair''')
        snake_case				=			[
            ('''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(A__    ,   tokenizer_r.encode    ,   A__    ,   max_length=A__    ,   padding='''max_length'''    )

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

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

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

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

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







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   pad_token='''<pad>'''    )

      # Simple input
      snake_case				=			'''This is a simple input'''
      snake_case				=			['''This is a simple input looooooooong''', '''This is a simple input''']
      snake_case				=			('''This is a simple input''', '''This is a pair''')
      snake_case				=			[
          ('''This is a simple input loooooong''', '''This is a simple input'''),
          ('''This is a simple pair loooooong''', '''This is a simple pair'''),
      ]

      snake_case				=			tokenizer.pad_token_id

      snake_case				=			tokenizer(A__    ,   padding='''max_length'''    ,   max_length=30    ,   return_tensors='''np'''    )
      snake_case				=			tokenizer(A__    ,   padding=A__    ,   truncate=A__    ,   return_tensors='''np'''    )
      snake_case				=			tokenizer(*A__    ,   padding='''max_length'''    ,   max_length=60    ,   return_tensors='''np'''    )
      snake_case				=			tokenizer(A__    ,   padding=A__    ,   truncate=A__    ,   return_tensors='''np'''    )

      # s
      # test single string max_length padding
      self.assertEqual(out_s['''input_ids'''].shape[-1]    ,   30    )
      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]    ,   33    )
      # 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]    ,   60    )
      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]    ,   52    )
      # 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     UpperCamelCase			( self    ) ->	str:
      snake_case				=			'''$$$'''
      snake_case				=			CodeGenTokenizer.from_pretrained(self.tmpdirname    ,   bos_token=A__    ,   add_bos_token=A__    )

      snake_case				=			'''This is a simple input'''
      snake_case				=			['''This is a simple input 1''', '''This is a simple input 2''']

      snake_case				=			tokenizer.bos_token_id

      snake_case				=			tokenizer(A__    )
      snake_case				=			tokenizer(A__    )

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

      snake_case				=			tokenizer.decode(out_s.input_ids    )
      snake_case				=			tokenizer.batch_decode(out_sa.input_ids    )

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







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''    )

      snake_case				=			'''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
      snake_case				=			'''\nif len_a > len_b:      result = a\nelse:      result = b'''

      snake_case				=			tokenizer.encode(A__    )
      snake_case				=			['''^#''', re.escape('''<|endoftext|>'''    ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
      snake_case				=			tokenizer.decode(A__    ,   truncate_before_pattern=A__    )
      self.assertEqual(A__    ,   A__    )







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

