
"""simple docstring"""

import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 	_lowercase       (    UpperCamelCase_    ,	unittest.TestCase		):
	lowercase_				=     BertTokenizer
	lowercase_				=     BertTokenizerFast
	lowercase_				=     True
	lowercase_				=     True
	lowercase_				=     filter_non_english




	def       _UpperCamelCase   (			self )		->							List[str]:
			super().setUp()

			lowerCamelCase					:      Dict          =   [
			    """[UNK]""",
			    """[CLS]""",
			    """[SEP]""",
			    """[PAD]""",
			    """[MASK]""",
			    """want""",
			    """##want""",
			    """##ed""",
			    """wa""",
			    """un""",
			    """runn""",
			    """##ing""",
			    """,""",
			    """low""",
			    """lowest""",
			]
			lowerCamelCase					:      Any          =   os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['vocab_file'] )
			with open(self.vocab_file		,			'w'		,			encoding='utf-8' ) as vocab_writer:
					vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      Any          =   """UNwant\u00E9d,running"""
			lowerCamelCase					:      str          =   """unwanted, running"""
			return input_text, output_text




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Any          =   self.tokenizer_class(self.vocab_file )

			lowerCamelCase					:      str          =   tokenizer.tokenize('UNwant\u00E9d,running' )
			self.assertListEqual(_a		,			['un', '##want', '##ed', ',', 'runn', '##ing'] )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_a )		,			[9, 6, 7, 12, 10, 11] )




	def       _UpperCamelCase   (			self )		->							int:


			if not self.test_rust_tokenizer:
					return

			lowerCamelCase					:      Optional[Any]          =   self.get_tokenizer()
			lowerCamelCase					:      List[Any]          =   self.get_rust_tokenizer()

			lowerCamelCase					:      Optional[Any]          =   """UNwant\u00E9d,running"""

			lowerCamelCase					:      Union[str, Any]          =   tokenizer.tokenize(_a )
			lowerCamelCase					:      Optional[Any]          =   rust_tokenizer.tokenize(_a )
			self.assertListEqual(_a		,			_a )

			lowerCamelCase					:      Union[str, Any]          =   tokenizer.encode(_a		,			add_special_tokens=_a )
			lowerCamelCase					:      List[str]          =   rust_tokenizer.encode(_a		,			add_special_tokens=_a )
			self.assertListEqual(_a		,			_a )

			lowerCamelCase					:      Any          =   self.get_rust_tokenizer()
			lowerCamelCase					:      Optional[int]          =   tokenizer.encode(_a )
			lowerCamelCase					:      int          =   rust_tokenizer.encode(_a )
			self.assertListEqual(_a		,			_a )

			# With lower casing
			lowerCamelCase					:      List[str]          =   self.get_tokenizer(do_lower_case=_a )
			lowerCamelCase					:      Tuple          =   self.get_rust_tokenizer(do_lower_case=_a )

			lowerCamelCase					:      int          =   """UNwant\u00E9d,running"""

			lowerCamelCase					:      List[str]          =   tokenizer.tokenize(_a )
			lowerCamelCase					:      List[Any]          =   rust_tokenizer.tokenize(_a )
			self.assertListEqual(_a		,			_a )

			lowerCamelCase					:      str          =   tokenizer.encode(_a		,			add_special_tokens=_a )
			lowerCamelCase					:      List[Any]          =   rust_tokenizer.encode(_a		,			add_special_tokens=_a )
			self.assertListEqual(_a		,			_a )

			lowerCamelCase					:      List[str]          =   self.get_rust_tokenizer()
			lowerCamelCase					:      List[str]          =   tokenizer.encode(_a )
			lowerCamelCase					:      Dict          =   rust_tokenizer.encode(_a )
			self.assertListEqual(_a		,			_a )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   BasicTokenizer()

			self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' )		,			['ah', '\u535A', '\u63A8', 'zz'] )




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      List[str]          =   BasicTokenizer(do_lower_case=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ' )		,			['hello', '!', 'how', 'are', 'you', '?'] )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )		,			['hello'] )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   BasicTokenizer(do_lower_case=_a		,			strip_accents=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )		,			['hällo', '!', 'how', 'are', 'you', '?'] )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )		,			['h\u00E9llo'] )




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      str          =   BasicTokenizer(do_lower_case=_a		,			strip_accents=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )		,			['hallo', '!', 'how', 'are', 'you', '?'] )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )		,			['hello'] )




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      List[str]          =   BasicTokenizer(do_lower_case=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )		,			['hallo', '!', 'how', 'are', 'you', '?'] )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )		,			['hello'] )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   BasicTokenizer(do_lower_case=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ' )		,			['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      str          =   BasicTokenizer(do_lower_case=_a		,			strip_accents=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )		,			['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      List[Any]          =   BasicTokenizer(do_lower_case=_a		,			strip_accents=_a )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )		,			['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      int          =   BasicTokenizer(do_lower_case=_a		,			never_split=['[UNK]'] )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]' )		,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      Union[str, Any]          =   BasicTokenizer()
			lowerCamelCase					:      Union[str, Any]          =   """a\n'll !!to?'d of, can't."""
			lowerCamelCase					:      str          =   ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
			self.assertListEqual(tokenizer.tokenize(_a )		,			_a )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			lowerCamelCase					:      int          =   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

			lowerCamelCase					:      Optional[int]          =   {}
			for i, token in enumerate(_a ):
					lowerCamelCase					:      int          =   i
			lowerCamelCase					:      Any          =   WordpieceTokenizer(vocab=_a		,			unk_token='[UNK]' )

			self.assertListEqual(tokenizer.tokenize('' )		,			[] )

			self.assertListEqual(tokenizer.tokenize('unwanted running' )		,			['un', '##want', '##ed', 'runn', '##ing'] )

			self.assertListEqual(tokenizer.tokenize('unwantedX running' )		,			['[UNK]', 'runn', '##ing'] )




	def       _UpperCamelCase   (			self )		->							Tuple:
			self.assertTrue(_is_whitespace(' ' ) )
			self.assertTrue(_is_whitespace('\t' ) )
			self.assertTrue(_is_whitespace('\r' ) )
			self.assertTrue(_is_whitespace('\n' ) )
			self.assertTrue(_is_whitespace('\u00A0' ) )

			self.assertFalse(_is_whitespace('A' ) )
			self.assertFalse(_is_whitespace('-' ) )




	def       _UpperCamelCase   (			self )		->							Any:
			self.assertTrue(_is_control('\u0005' ) )

			self.assertFalse(_is_control('A' ) )
			self.assertFalse(_is_control(' ' ) )
			self.assertFalse(_is_control('\t' ) )
			self.assertFalse(_is_control('\r' ) )




	def       _UpperCamelCase   (			self )		->							List[str]:
			self.assertTrue(_is_punctuation('-' ) )
			self.assertTrue(_is_punctuation('$' ) )
			self.assertTrue(_is_punctuation('`' ) )
			self.assertTrue(_is_punctuation('.' ) )

			self.assertFalse(_is_punctuation('A' ) )
			self.assertFalse(_is_punctuation(' ' ) )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      List[Any]          =   self.get_tokenizer()
			lowerCamelCase					:      Optional[Any]          =   self.get_rust_tokenizer()

			# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
			self.assertListEqual([tokenizer.tokenize(_a ) for t in ['Test', '\xad', 'test']]		,			[['[UNK]'], [], ['[UNK]']] )

			self.assertListEqual(
			    [rust_tokenizer.tokenize(_a ) for t in ['Test', '\xad', 'test']]		,			[['[UNK]'], [], ['[UNK]']] )




	@slow
	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      str          =   self.tokenizer_class.from_pretrained('bert-base-uncased' )

			lowerCamelCase					:      Optional[Any]          =   tokenizer.encode('sequence builders'		,			add_special_tokens=_a )
			lowerCamelCase					:      str          =   tokenizer.encode('multi-sequence build'		,			add_special_tokens=_a )

			lowerCamelCase					:      Any          =   tokenizer.build_inputs_with_special_tokens(_a )
			lowerCamelCase					:      Optional[Any]          =   tokenizer.build_inputs_with_special_tokens(_a		,			_a )

			assert encoded_sentence == [101] + text + [102]
			assert encoded_pair == [101] + text + [102] + text_a + [102]




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


			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
							lowerCamelCase					:      str          =   self.rust_tokenizer_class.from_pretrained(_a		,			**_a )

							lowerCamelCase					:      List[Any]          =   F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
							lowerCamelCase					:      Tuple          =   tokenizer_r.encode_plus(
							    _a		,			return_attention_mask=_a		,			return_token_type_ids=_a		,			return_offsets_mapping=_a		,			add_special_tokens=_a		,			)

							lowerCamelCase					:      Tuple          =   tokenizer_r.do_lower_case if hasattr(_a		,			'do_lower_case' ) else False
							lowerCamelCase					:      str          =   (
							    [
							        ((0, 0), tokenizer_r.cls_token),
							        ((0, 1), """A"""),
							        ((1, 2), ""","""),
							        ((3, 5), """na"""),
							        ((5, 6), """##ï"""),
							        ((6, 8), """##ve"""),
							        ((9, 15), tokenizer_r.mask_token),
							        ((16, 21), """Allen"""),
							        ((21, 23), """##NL"""),
							        ((23, 24), """##P"""),
							        ((25, 33), """sentence"""),
							        ((33, 34), """."""),
							        ((0, 0), tokenizer_r.sep_token),
							    ]
							    if not do_lower_case
							    else [
							        ((0, 0), tokenizer_r.cls_token),
							        ((0, 1), """a"""),
							        ((1, 2), ""","""),
							        ((3, 8), """naive"""),
							        ((9, 15), tokenizer_r.mask_token),
							        ((16, 21), """allen"""),
							        ((21, 23), """##nl"""),
							        ((23, 24), """##p"""),
							        ((25, 33), """sentence"""),
							        ((33, 34), """."""),
							        ((0, 0), tokenizer_r.sep_token),
							    ]
							)

							self.assertEqual(
							    [e[1] for e in expected_results]		,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
							self.assertEqual([e[0] for e in expected_results]		,			tokens['offset_mapping'] )




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      List[Any]          =   ["""的""", """人""", """有"""]
			lowerCamelCase					:      List[str]          =   """""".join(_a )
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
							lowerCamelCase					:      Any          =   True
							lowerCamelCase					:      Optional[Any]          =   self.tokenizer_class.from_pretrained(_a		,			**_a )
							lowerCamelCase					:      Optional[Any]          =   self.rust_tokenizer_class.from_pretrained(_a		,			**_a )

							lowerCamelCase					:      Optional[int]          =   tokenizer_p.encode(_a		,			add_special_tokens=_a )
							lowerCamelCase					:      Tuple          =   tokenizer_r.encode(_a		,			add_special_tokens=_a )

							lowerCamelCase					:      Optional[int]          =   tokenizer_r.convert_ids_to_tokens(_a )
							lowerCamelCase					:      int          =   tokenizer_p.convert_ids_to_tokens(_a )

							# it is expected that each Chinese character is not preceded by "##"
							self.assertListEqual(_a		,			_a )
							self.assertListEqual(_a		,			_a )

							lowerCamelCase					:      Tuple          =   False
							lowerCamelCase					:      Dict          =   self.rust_tokenizer_class.from_pretrained(_a		,			**_a )
							lowerCamelCase					:      str          =   self.tokenizer_class.from_pretrained(_a		,			**_a )

							lowerCamelCase					:      Tuple          =   tokenizer_r.encode(_a		,			add_special_tokens=_a )
							lowerCamelCase					:      List[str]          =   tokenizer_p.encode(_a		,			add_special_tokens=_a )

							lowerCamelCase					:      str          =   tokenizer_r.convert_ids_to_tokens(_a )
							lowerCamelCase					:      Dict          =   tokenizer_p.convert_ids_to_tokens(_a )

							# it is expected that only the first Chinese character is not preceded by "##".
							lowerCamelCase					:      int          =   [
							    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_a )
							]
							self.assertListEqual(_a		,			_a )
							self.assertListEqual(_a		,			_a )








"""simple docstring"""

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_A						=			logging.get_logger('transformers.models.speecht5')
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		hf_model.apply_weight_norm()

		lowerCamelCase					:      str          =   checkpoint['input_conv.weight_g']
		lowerCamelCase					:      int          =   checkpoint['input_conv.weight_v']
		lowerCamelCase					:      Optional[Any]          =   checkpoint['input_conv.bias']

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

						lowerCamelCase					:      Dict          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
						lowerCamelCase					:      str          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
						lowerCamelCase					:      Optional[Any]          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

		lowerCamelCase					:      Any          =   checkpoint['output_conv.1.weight_g']
		lowerCamelCase					:      Tuple          =   checkpoint['output_conv.1.weight_v']
		lowerCamelCase					:      int          =   checkpoint['output_conv.1.bias']

		hf_model.remove_weight_norm()
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase					:      str          =   SpeechTaHifiGanConfig.from_pretrained(a_      )
		else:
				lowerCamelCase					:      Dict          =   SpeechTaHifiGanConfig()

		lowerCamelCase					:      int          =   SpeechTaHifiGan(a_      )

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

		lowerCamelCase					:      Tuple          =   np.load(a_      )
		lowerCamelCase					:      str          =   stats[0].reshape(-1      )
		lowerCamelCase					:      Optional[int]          =   stats[1].reshape(-1      )
		lowerCamelCase					:      Dict          =   torch.from_numpy(a_      ).float()
		lowerCamelCase					:      Optional[int]          =   torch.from_numpy(a_      ).float()

		model.save_pretrained(a_      )

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


if __name__ == "__main__":
					_A						=			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.'
					)

					_A						=			parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)






