


"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {"vocab_file": "spm_char.model"}

lowercase_			     =  {
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

lowercase_			     =  {
    "microsoft/speecht5_asr": 1_0_2_4,
    "microsoft/speecht5_tts": 1_0_2_4,
    "microsoft/speecht5_vc": 1_0_2_4,
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				VOCAB_FILES_NAMES
		__UpperCAmelCase		:			Union[str, Any]										=				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase		:			Optional[int]										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCAmelCase		:			Tuple										=				['input_ids', 'attention_mask']

		def __init__(  self							, _a							, _a="<s>"							, _a="</s>"							, _a="<unk>"							, _a="<pad>"							, _a = None							, **_a							, ):
				__a           =			{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=_a							, eos_token=_a							, unk_token=_a							, pad_token=_a							, sp_model_kwargs=self.sp_model_kwargs							, **_a							, )

				__a           =			vocab_file

				__a           =			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.Load(_a      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.sp_model.get_piece_size()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			{self.convert_ids_to_tokens(_a      ): i for i in range(self.vocab_size      )}
				vocab.update(self.added_tokens_encoder      )
				return vocab

		def __getstate__(  self      ):
				__a           =			self.__dict__.copy()
				__a           =			None
				return state

		def __setstate__(  self							, _a      ):
				__a           =			d

				# for backward compatibility
				if not hasattr(self							, '''sp_model_kwargs'''      ):
						__a           =			{}

				__a           =			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.Load(self.vocab_file      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.sp_model.encode(_a							, out_type=_a      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.sp_model.piece_to_id(_a      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.sp_model.IdToPiece(_a      )
				return token

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			[]
				__a           =			''''''
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								out_string += self.sp_model.decode(_a      ) + token
								__a           =			[]
						else:
								current_sub_tokens.append(_a      )
				out_string += self.sp_model.decode(_a      )
				return out_string.strip()

		def 			__UpperCAmelCase       (  self							, _a							, _a=None      ):
				if token_ids_a is None:
						return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]

		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = 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      )

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





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

				if os.path.abspath(self.vocab_file      ) != os.path.abspath(_a      ) and os.path.isfile(self.vocab_file      ):
						copyfile(self.vocab_file							, _a      )
				elif not os.path.isfile(self.vocab_file      ):
						with open(_a							, '''wb'''      ) as fi:
								__a           =			self.sp_model.serialized_model_proto()
								fi.write(_a      )

				return (out_vocab_file,)












from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
 lowerCamelCase				:		int     =   input("Enter image url: ").strip()
 print(F"""Downloading image from {url} ...""")
 lowerCamelCase				:		Tuple     =   BeautifulSoup(requests.get(url).content, "html.parser")
 # The image URL is in the content field of the first meta tag with property og:image
 lowerCamelCase				:		int     =   soup.find("meta", {"property": "og:image"})["content"]
 lowerCamelCase				:		Dict     =   requests.get(image_url).content
 lowerCamelCase				:		Optional[int]     =   F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
 with open(file_name, "wb") as fp:
  fp.write(image_data)
 print(F"""Done. Image saved to disk as {file_name}.""")



