


'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase  :		Optional[Any]		     =	logging.get_logger(__name__)


_lowerCAmelCase  :		int		     =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

_lowerCAmelCase  :		Dict		     =	{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

_lowerCAmelCase  :		Any		     =	{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 			__UpperCamelCase    (    )    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= (
	    list(range(ord('!'				)					,   ord('~'				) + 1				)				) + list(range(ord('¡'				)					,   ord('¬'				) + 1				)				) + list(range(ord('®'				)					,   ord('ÿ'				) + 1				)				)
	)
	lowerCAmelCase					:    Any      							= bs[:]
	lowerCAmelCase					:    Optional[int]      							= 0
	for b in range(2**8				):
		if b not in bs:
			bs.append(UpperCamelCase__				)
			cs.append(2**8 + n				)
			n += 1
	lowerCAmelCase					:    Tuple      							= [chr(UpperCamelCase__				) for n in cs]
	return dict(zip(UpperCamelCase__					,   UpperCamelCase__				)				)

def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= set()
	lowerCAmelCase					:    Optional[Any]      							= word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)				)
		lowerCAmelCase					:    List[Any]      							= char
	return pairs






class  lowerCAmelCase				(				__lowercase							):
	_lowerCamelCase   :					Dict			      = VOCAB_FILES_NAMES
	_lowerCamelCase   :					Dict			      = PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase   :					List[str]			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase   :					Optional[Any]			      = ['''input_ids''', '''attention_mask''']



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__="replace" ,					snake_case__="<s>" ,					snake_case__="</s>" ,					snake_case__="</s>" ,					snake_case__="<s>" ,					snake_case__="<unk>" ,					snake_case__="<pad>" ,					snake_case__="<mask>" ,					snake_case__=False ,					**snake_case__ ,					):
		lowerCAmelCase					:    List[str]      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else bos_token
		lowerCAmelCase					:    Union[str, Any]      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else eos_token
		lowerCAmelCase					:    Optional[int]      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else sep_token
		lowerCAmelCase					:    List[str]      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else cls_token
		lowerCAmelCase					:    Any      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else unk_token
		lowerCAmelCase					:    Union[str, Any]      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else pad_token

		# Mask token behave like a normal word, i.e. include the space before it
		lowerCAmelCase					:    Union[str, Any]      							= AddedToken(__a ,					lstrip=__a ,					rstrip=__a    ) if isinstance(__a ,					__a    ) else mask_token

		super().__init__(
		    errors=__a ,					bos_token=__a ,					eos_token=__a ,					unk_token=__a ,					sep_token=__a ,					cls_token=__a ,					pad_token=__a ,					mask_token=__a ,					add_prefix_space=__a ,					**__a ,					)

		with open(__a ,					encoding='utf-8'    ) as vocab_handle:
			lowerCAmelCase					:    Optional[int]      							= json.load(__a    )
		lowerCAmelCase					:    int      							= {v: k for k, v in self.encoder.items()}
		lowerCAmelCase					:    Union[str, Any]      							= errors  # how to handle errors in decoding
		lowerCAmelCase					:    Any      							= bytes_to_unicode()
		lowerCAmelCase					:    int      							= {v: k for k, v in self.byte_encoder.items()}
		with open(__a ,					encoding='utf-8'    ) as merges_handle:
			lowerCAmelCase					:    Any      							= merges_handle.read().split('\n'    )[1:-1]
		lowerCAmelCase					:    Dict      							= [tuple(merge.split()    ) for merge in bpe_merges]
		lowerCAmelCase					:    Dict      							= dict(zip(__a ,					range(len(__a    )    )    )    )
		lowerCAmelCase					:    Optional[int]      							= {}
		lowerCAmelCase					:    Tuple      							= add_prefix_space

		# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
		lowerCAmelCase					:    Dict      							= re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )



	@property
	def 				lowercase     (					self    ):
		return len(self.encoder    )



	def 				lowercase     (					self    ):
		return dict(self.encoder ,					**self.added_tokens_encoder    )



	def 				lowercase     (					self ,					snake_case__    ):
		if token in self.cache:
			return self.cache[token]
		lowerCAmelCase					:    Any      							= tuple(__a    )
		lowerCAmelCase					:    Dict      							= get_pairs(__a    )

		if not pairs:
			return token

		while True:
			lowerCAmelCase					:    List[str]      							= min(__a ,					key=lambda snake_case__    : self.bpe_ranks.get(__a ,					float('inf'    )    )    )
			if bigram not in self.bpe_ranks:
				break
			lowerCAmelCase					:    Optional[Any]      							= bigram
			lowerCAmelCase					:    Union[str, Any]      							= []
			lowerCAmelCase					:    Optional[Any]      							= 0
			while i < len(__a    ):
				try:
					lowerCAmelCase					:    Optional[Any]      							= word.index(__a ,					__a    )
				except ValueError:
					new_word.extend(word[i:]    )
					break
				else:
					new_word.extend(word[i:j]    )
					lowerCAmelCase					:    Any      							= j

				if word[i] == first and i < len(__a    ) - 1 and word[i + 1] == second:
					new_word.append(first + second    )
					i += 2
				else:
					new_word.append(word[i]    )
					i += 1
			lowerCAmelCase					:    Optional[int]      							= tuple(__a    )
			lowerCAmelCase					:    List[str]      							= new_word
			if len(__a    ) == 1:
				break
			else:
				lowerCAmelCase					:    Union[str, Any]      							= get_pairs(__a    )
		lowerCAmelCase					:    Union[str, Any]      							= """ """.join(__a    )
		lowerCAmelCase					:    Dict      							= word
		return word



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    int      							= []
		for token in re.findall(self.pat ,					__a    ):
			lowerCAmelCase					:    str      							= """""".join(
			    self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__a    ).split(' '    )    )
		return bpe_tokens



	def 				lowercase     (					self ,					snake_case__    ):
		return self.encoder.get(__a ,					self.encoder.get(self.unk_token    )    )



	def 				lowercase     (					self ,					snake_case__    ):
		return self.decoder.get(__a    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= """""".join(__a    )
		lowerCAmelCase					:    Optional[int]      							= bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8' ,					errors=self.errors    )
		return text



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		if not os.path.isdir(__a    ):
			logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
			return
		lowerCAmelCase					:    int      							= os.path.join(
		    __a ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
		lowerCAmelCase					:    Optional[int]      							= os.path.join(
		    __a ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

		with open(__a ,					'w' ,					encoding='utf-8'    ) as f:
			f.write(json.dumps(self.encoder ,					indent=2 ,					sort_keys=__a ,					ensure_ascii=__a    ) + '\n'    )

		lowerCAmelCase					:    List[Any]      							= 0
		with open(__a ,					'w' ,					encoding='utf-8'    ) as writer:
			writer.write('#version: 0.2\n'    )
			for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,					key=lambda snake_case__    : kv[1]    ):
				if index != token_index:
					logger.warning(
					    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
					    ' Please check that the tokenizer is not corrupted!'    )
					lowerCAmelCase					:    Tuple      							= token_index
				writer.write(' '.join(__a    ) + '\n'    )
				index += 1

		return vocab_file, merge_file



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowerCAmelCase					:    Any      							= [self.cls_token_id]
		lowerCAmelCase					:    Dict      							= [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None ,					snake_case__ = False    ):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=__a ,					token_ids_a=__a ,					already_has_special_tokens=__a    )

		if token_ids_a is None:
			return [1] + ([0] * len(__a    )) + [1]
		return [1] + ([0] * len(__a    )) + [1, 1] + ([0] * len(__a    )) + [1]



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		lowerCAmelCase					:    int      							= [self.sep_token_id]
		lowerCAmelCase					:    List[str]      							= [self.cls_token_id]

		if token_ids_a is None:
			return len(cls + token_ids_a + sep    ) * [0]
		return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=False ,					**snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= kwargs.pop('add_prefix_space' ,					self.add_prefix_space    )
		if (is_split_into_words or add_prefix_space) and (len(__a    ) > 0 and not text[0].isspace()):
			lowerCAmelCase					:    str      							= """ """ + text
		return (text, kwargs)




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

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

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

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

	hf_model.remove_weight_norm()

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




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    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__":
				_lowerCAmelCase  :		List[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  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
