


"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
	from ...tokenization_utils_base import TextInput
from ...utils import logging


A  :     List[str]    					=    logging.get_logger(__name__)

A  :     Tuple    					=    {"vocab_file": "spiece.model"}

A  :     Tuple    					=    {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A  :     Tuple    					=    {
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}

A  :     List[Any]    					=    "▁"
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[Any]          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					List[Any]          =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					Dict          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Dict          =["""input_ids""", """attention_mask"""]

							def __init__(  self					,     __a					,     __a="</s>"					,     __a="<unk>"					,     __a="<pad>"					,     __a=1_00					,     __a=None					,     __a = None					,     __a=True					,     **__a					,     ):
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
													__lowerCAmelCase       					=    [f"<extra_id_{i}>" for i in range(__a			)]
										elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													__lowerCAmelCase       					=    len(set(filter(lambda __a			: bool("extra_id" in str(__a			)			)					,     __a			)			)			)
													if extra_tokens != extra_ids:
																raise ValueError(
																    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																    " tokens"			)
										if legacy:
													logger.warning_once(
													    f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
													    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"			)

										__lowerCAmelCase       					=    legacy
										__lowerCAmelCase       					=    {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=__a					,     unk_token=__a					,     pad_token=__a					,     extra_ids=__a					,     additional_special_tokens=__a					,     sp_model_kwargs=self.sp_model_kwargs					,     legacy=__a					,     **__a					,     )

										__lowerCAmelCase       					=    vocab_file
										__lowerCAmelCase       					=    extra_ids

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

							@staticmethod
							def     snake_case       (  __a					,     __a					,     __a			):
										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
													__lowerCAmelCase       					=    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
													if init_max_model_length is not None and init_max_model_length != max_model_length:
																return init_max_model_length
													elif init_max_model_length is None:
																warnings.warn(
																    "This tokenizer was incorrectly instantiated with a model max length of"
																    f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																    f" {pretrained_model_name_or_path} automatically truncating your input to"
																    f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																    f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																    " instantiate this tokenizer with `model_max_length` set to your preferred value."					,     __a					,     )

										return max_model_length

							@property
							def     snake_case       (  self			):
										return self.sp_model.get_piece_size() + self._extra_ids

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

							def     snake_case       (  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			)

										# normal case: some special tokens
										if token_ids_a is None:
													return ([0] * len(__a			)) + [1]
										return ([0] * len(__a			)) + [1] + ([0] * len(__a			)) + [1]

							def     snake_case       (  self			):
										return list(
										    set(filter(lambda __a			: bool(re.search(R"<extra_id_\d+>"					,     __a			)			) is not None					,     self.additional_special_tokens			)			)			)

							def     snake_case       (  self			):
										return [self._convert_token_to_id(__a			) for token in self.get_sentinel_tokens()]

							def     snake_case       (  self					,     __a			):
										if len(__a			) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
													    " eos tokens being added."			)
													return token_ids
										else:
													return token_ids + [self.eos_token_id]

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    [self.eos_token_id]

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

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    self._add_eos_if_not_present(__a			)
										if token_ids_a is None:
													return token_ids_a
										else:
													__lowerCAmelCase       					=    self._add_eos_if_not_present(__a			)
													return token_ids_a + token_ids_a

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

							def __setstate__(  self					,     __a			):
										__lowerCAmelCase       					=    d

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

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

							def     snake_case       (  self					,     __a					,     **__a			):
										# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
										# the beginning of the text
										if not self.legacy:
													__lowerCAmelCase       					=    SPIECE_UNDERLINE + text.replace(__a					,     " "			)
										return super().tokenize(__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     **__a			):
										if not self.legacy:
													__lowerCAmelCase       					=    text.startswith(__a			)
													if is_first:
																__lowerCAmelCase       					=    text[1:]

										__lowerCAmelCase       					=    self.sp_model.encode(__a					,     out_type=__a			)

										if not self.legacy and not is_first and not text.startswith(" "			) and tokens[0].startswith(__a			):
													__lowerCAmelCase       					=    ([tokens[0][1:]] if len(tokens[0]			) > 1 else []) + tokens[1:]
										return tokens

							def     snake_case       (  self					,     __a			):
										if token.startswith("<extra_id_"			):
													__lowerCAmelCase       					=    re.match(R"<extra_id_(\d+)>"					,     __a			)
													__lowerCAmelCase       					=    int(match.group(1			)			)
													return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(__a			)

							def     snake_case       (  self					,     __a			):
										if index < self.sp_model.get_piece_size():
													__lowerCAmelCase       					=    self.sp_model.IdToPiece(__a			)
										else:
													__lowerCAmelCase       					=    f"<extra_id_{self.vocab_size - 1 - index}>"
										return token

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



							def     snake_case       (  self					,     __a					,     __a = None			):
										if not os.path.isdir(__a			):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
													return
										__lowerCAmelCase       					=    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:
																__lowerCAmelCase       					=    self.sp_model.serialized_model_proto()
																fi.write(__a			)

										return (out_vocab_file,)








"""simple docstring"""


import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

A  :     Union[str, Any]    					=    {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}


def _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    min(_UpperCamelCase					,				50   )  # Prevent abuse!
			__lowerCAmelCase       					=    {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

			__lowerCAmelCase       					=    requests.get("https://www.google.com/search"					,				params=_UpperCamelCase					,				headers=_UpperCamelCase   )
			__lowerCAmelCase       					=    BeautifulSoup(html.text					,				"html.parser"   )
			__lowerCAmelCase       					=    "".join(
			    re.findall(R"AF_initDataCallback\(([^<]+)\);"					,				str(soup.select("script"   )   )   )   )

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

			__lowerCAmelCase       					=    re.findall(
			    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","					,				_UpperCamelCase					,				)
			if not matched_google_image_data:
						return 0

			__lowerCAmelCase       					=    re.sub(
			    R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"					,				""					,				str(_UpperCamelCase   )					,				)

			__lowerCAmelCase       					=    re.findall(
			    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"					,				_UpperCamelCase					,				)
			for index, fixed_full_res_image in enumerate(_UpperCamelCase   ):
						if index >= max_images:
									return index
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    urllib.request.build_opener()
						__lowerCAmelCase       					=    [
						    (
						        "User-Agent",
						        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
						        " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
						    )
						]
						urllib.request.install_opener(_UpperCamelCase   )
						__lowerCAmelCase       					=    f"query_{query.replace(' '					,				'_'   )}"
						if not os.path.exists(_UpperCamelCase   ):
									os.makedirs(_UpperCamelCase   )
						urllib.request.urlretrieve(  # noqa: S310
						    _UpperCamelCase					,				f"{path_name}/original_size_img_{index}.jpg"   )
			return index


if __name__ == "__main__":
	try:
		A  :     Any    					=    download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print("Please provide a search term.")
		raise




