






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 							__a							(  __A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						MvpTokenizer
		UpperCAmelCase__      :				Any				    =						MvpTokenizerFast
		UpperCAmelCase__      :				Optional[int]				    =						True
		UpperCAmelCase__      :				Tuple				    =						filter_roberta_detectors


		def   __snake_case			(						self       ):
						super().setUp()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   dict(zip(UpperCAmelCase__ ,    range(len(UpperCAmelCase__       )       )       )       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {'unk_token': '<unk>'}

						SCREAMING_SNAKE_CASE_   :	Tuple													=   os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['vocab_file']       )
						SCREAMING_SNAKE_CASE_   :	int													=   os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['merges_file']       )
						with open(self.vocab_file ,    'w' ,    encoding='utf-8'       ) as fp:
										fp.write(json.dumps(UpperCAmelCase__       ) + '\n'       )
						with open(self.merges_file ,    'w' ,    encoding='utf-8'       ) as fp:
										fp.write('\n'.join(UpperCAmelCase__       )       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						kwargs.update(self.special_tokens_map       )
						return self.tokenizer_class.from_pretrained(self.tmpdirname ,    **UpperCAmelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						kwargs.update(self.special_tokens_map       )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,    **UpperCAmelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return "lower newer", "lower newer"


		@cached_property
		def   __snake_case			(						self       ):
						return MvpTokenizer.from_pretrained('RUCAIBox/mvp'       )


		@cached_property
		def   __snake_case			(						self       ):
						return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
						SCREAMING_SNAKE_CASE_   :	int													=   [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer(UpperCAmelCase__ ,    max_length=len(UpperCAmelCase__       ) ,    padding=UpperCAmelCase__ ,    return_tensors='pt'       )
										self.assertIsInstance(UpperCAmelCase__ ,    UpperCAmelCase__       )

										self.assertEqual((2, 9) ,    batch.input_ids.shape       )
										self.assertEqual((2, 9) ,    batch.attention_mask.shape       )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   batch.input_ids.tolist()[0]
										self.assertListEqual(UpperCAmelCase__ ,    UpperCAmelCase__       )
										# Test that special tokens are reset


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer(UpperCAmelCase__ ,    padding=UpperCAmelCase__ ,    return_tensors='pt'       )
										# check if input_ids are returned and no labels
										self.assertIn('input_ids' ,    UpperCAmelCase__       )
										self.assertIn('attention_mask' ,    UpperCAmelCase__       )
										self.assertNotIn('labels' ,    UpperCAmelCase__       )
										self.assertNotIn('decoder_attention_mask' ,    UpperCAmelCase__       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   [
						    'Summary of the text.',
						    'Another summary.',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_   :	str													=   tokenizer(text_target=UpperCAmelCase__ ,    max_length=32 ,    padding='max_length' ,    return_tensors='pt'       )
										self.assertEqual(32 ,    targets['input_ids'].shape[1]       )


		@require_torch
		def   __snake_case			(						self       ):
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer(
										    ['I am a small frog' * 1024, 'I am a small frog'] ,    padding=UpperCAmelCase__ ,    truncation=UpperCAmelCase__ ,    return_tensors='pt'       )
										self.assertIsInstance(UpperCAmelCase__ ,    UpperCAmelCase__       )
										self.assertEqual(batch.input_ids.shape ,    (2, 1024)       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ['A long paragraph for summarization.']
						SCREAMING_SNAKE_CASE_   :	Any													=   [
						    'Summary of the text.',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer(UpperCAmelCase__ ,    text_target=UpperCAmelCase__ ,    return_tensors='pt'       )
										SCREAMING_SNAKE_CASE_   :	int													=   inputs['input_ids']
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   inputs['labels']
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )


		def   __snake_case			(						self       ):
						pass






		def   __snake_case			(						self       ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ ,    **UpperCAmelCase__       )
														SCREAMING_SNAKE_CASE_   :	Dict													=   self.tokenizer_class.from_pretrained(UpperCAmelCase__ ,    **UpperCAmelCase__       )
														SCREAMING_SNAKE_CASE_   :	Dict													=   'A, <mask> AllenNLP sentence.'
														SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer_r.encode_plus(UpperCAmelCase__ ,    add_special_tokens=UpperCAmelCase__ ,    return_token_type_ids=UpperCAmelCase__       )
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   tokenizer_p.encode_plus(UpperCAmelCase__ ,    add_special_tokens=UpperCAmelCase__ ,    return_token_type_ids=UpperCAmelCase__       )

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['token_type_ids']       ) ,    sum(tokens_p['token_type_ids']       )       )

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       ) ,    sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       ) ,    )

														SCREAMING_SNAKE_CASE_   :	int													=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
														SCREAMING_SNAKE_CASE_   :	str													=   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['input_ids'] ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )
														self.assertSequenceEqual(tokens_r['input_ids'] ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )

														self.assertSequenceEqual(
														    UpperCAmelCase__ ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
														self.assertSequenceEqual(
														    UpperCAmelCase__ ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A        =			input('Enter image url: ').strip()
				print(f"""Downloading image from {url} ...""")
				A        =			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A        =			soup.find('meta', {'property': 'og:image'})['content']
				A        =			requests.get(image_url).content
				A        =			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}.""")