





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase	:					List[str]			  =					get_tests_dir("""fixtures/test_sentencepiece.model""")
__lowerCamelCase	:					Union[str, Any]			  =					get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

__lowerCamelCase	:					List[str]			  =					"""pt""" if is_torch_available() else """tf"""



@require_sentencepiece
@require_tokenizers
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :List[str]						   =       CamembertTokenizer
      _UpperCAmelCase     :int						   =       CamembertTokenizerFast
      _UpperCAmelCase     :Optional[Any]						   =       True
      _UpperCAmelCase     :int						   =       True





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase     : Tuple	     =       CamembertTokenizer(A_       )
       tokenizer.save_pretrained(self.tmpdirname       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       "<pad>"
       UpperCamelCase     : Union[str, Any]	     =       1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_       )	,							A_       )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_       )	,							A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       list(self.get_tokenizer().get_vocab().keys()       )

       self.assertEqual(vocab_keys[0]	,							"<s>NOTUSED"       )
       self.assertEqual(vocab_keys[1]	,							"<pad>"       )
       self.assertEqual(vocab_keys[-1]	,							"<mask>"       )
       self.assertEqual(len(A_       )	,							1004       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size	,							1005       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       CamembertTokenizer(A_       )
       tokenizer.save_pretrained(self.tmpdirname       )
       UpperCamelCase     : List[str]	     =       CamembertTokenizerFast.from_pretrained(self.tmpdirname       )

       UpperCamelCase     : Optional[Any]	     =       "I was born in 92000, and this is falsé."

       UpperCamelCase     : Any	     =       tokenizer.encode(A_       )
       UpperCamelCase     : Optional[Any]	     =       rust_tokenizer.encode(A_       )
       self.assertListEqual(A_	,							A_       )

       UpperCamelCase     : Any	     =       tokenizer.encode(A_	,							add_special_tokens=A_       )
       UpperCamelCase     : int	     =       rust_tokenizer.encode(A_	,							add_special_tokens=A_       )
       self.assertListEqual(A_	,							A_       )

       # <unk> tokens are not the same for `rust` than for `slow`.
       # Because spm gives back raw token instead of `unk` in EncodeAsPieces
       # tokens = tokenizer.tokenize(sequence)
       UpperCamelCase     : Optional[Any]	     =       tokenizer.convert_ids_to_tokens(A_       )
       UpperCamelCase     : Dict	     =       rust_tokenizer.tokenize(A_       )
       self.assertListEqual(A_	,							A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if not self.test_rust_tokenizer:
        return

       UpperCamelCase     : Tuple	     =       self.get_tokenizer()
       UpperCamelCase     : Dict	     =       self.get_rust_tokenizer()

       UpperCamelCase     : Optional[Any]	     =       "I was born in 92000, and this is falsé."

       UpperCamelCase     : Union[str, Any]	     =       tokenizer.tokenize(A_       )
       UpperCamelCase     : Dict	     =       rust_tokenizer.tokenize(A_       )
       self.assertListEqual(A_	,							A_       )

       UpperCamelCase     : int	     =       tokenizer.encode(A_	,							add_special_tokens=A_       )
       UpperCamelCase     : str	     =       rust_tokenizer.encode(A_	,							add_special_tokens=A_       )
       self.assertListEqual(A_	,							A_       )

       UpperCamelCase     : List[str]	     =       self.get_rust_tokenizer()
       UpperCamelCase     : Union[str, Any]	     =       tokenizer.encode(A_       )
       UpperCamelCase     : Optional[int]	     =       rust_tokenizer.encode(A_       )
       self.assertListEqual(A_	,							A_       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       {"input_ids": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # camembert is a french model. So we also use french texts.
       UpperCamelCase     : Dict	     =       [
           "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
           "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
           "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
           "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
           "telles que la traduction et la synthèse de texte.",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=A_	,							model_name="camembert-base"	,							revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"	,							sequences=A_	,							)







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

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

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : 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__":
    __lowerCamelCase	:					Union[str, Any]			  =					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."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
