




'''simple docstring'''



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Union[str, Any]        =						MvpTokenizer
       A						:				Union[str, Any]        =						MvpTokenizerFast
       A						:				Tuple        =						True
       A						:				List[Any]        =						filter_roberta_detectors







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						dict(zip(A,							range(len(A  )  )  )  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{'unk_token': '<unk>'}

             SCREAMING_SNAKE_CASE   :      str    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file']  )
             SCREAMING_SNAKE_CASE   :      Any    					=						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  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return "lower newer", "lower newer"







       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )







       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             SCREAMING_SNAKE_CASE   :      List[str]    					=						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer(A,							max_length=len(A  ),							padding=A,							return_tensors='pt'  )
                   self.assertIsInstance(A,							A  )

                   self.assertEqual((2, 9),							batch.input_ids.shape  )
                   self.assertEqual((2, 9),							batch.attention_mask.shape  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						batch.input_ids.tolist()[0]
                   self.assertListEqual(A,							A  )
                   # Test that special tokens are reset







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer(A,							padding=A,							return_tensors='pt'  )
                   # check if input_ids are returned and no labels
                   self.assertIn('input_ids',							A  )
                   self.assertIn('attention_mask',							A  )
                   self.assertNotIn('labels',							A  )
                   self.assertNotIn('decoder_attention_mask',							A  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[
                 'Summary of the text.',
                 'Another summary.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      int    					=						tokenizer(text_target=A,							max_length=32,							padding='max_length',							return_tensors='pt'  )
                   self.assertEqual(32,							targets['input_ids'].shape[1]  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer(
                       ['I am a small frog' * 1_024, 'I am a small frog'],							padding=A,							truncation=A,							return_tensors='pt'  )
                   self.assertIsInstance(A,							A  )
                   self.assertEqual(batch.input_ids.shape,							(2, 1_024)  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						['A long paragraph for summarization.']
             SCREAMING_SNAKE_CASE   :      Dict    					=						[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer(A,							text_target=A,							return_tensors='pt'  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						inputs['input_ids']
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						inputs['labels']
                   self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                   self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                   self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                   self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						'A, <mask> AllenNLP sentence.'
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer_r.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_p.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )

                         # token_type_ids should put 0 everywhere
                         self.assertEqual(sum(tokens_r['token_type_ids']  ),							sum(tokens_p['token_type_ids']  )  )

                         # attention_mask should put 1 everywhere, so sum over length should be 1
                         self.assertEqual(
                             sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  ),							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  ),							)

                         SCREAMING_SNAKE_CASE   :      int    					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                         # Rust correctly handles the space before the mask while python doesnt
                         self.assertSequenceEqual(tokens_p['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
                         self.assertSequenceEqual(tokens_r['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )








'''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   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, 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    )    ):
                  SCREAMING_SNAKE_CASE   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						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   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

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

      hf_model.remove_weight_norm()




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						SpeechTaHifiGanConfig()

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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__":
      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,
      )


