
"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Any:
							A						   =   tempfile.mkdtemp()

							A						   =   BlipImageProcessor()
							A						   =   BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'      )

							A						   =   BlipProcessor(A_  ,A_      )

							processor.save_pretrained(self.tmpdirname      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,**A_     :      Dict      )     ->   Dict:
							return AutoProcessor.from_pretrained(self.tmpdirname  ,**A_      ).tokenizer


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,**A_     :      Tuple      )     ->   Tuple:
							return AutoProcessor.from_pretrained(self.tmpdirname  ,**A_      ).image_processor


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Optional[Any]:
							shutil.rmtree(self.tmpdirname      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   [np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta      )]

							A						   =   [Image.fromarray(np.moveaxis(A_  ,0  ,-1      )      ) for x in image_inputs]

							return image_inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Tuple:
							A						   =   BlipProcessor(tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							A						   =   self.get_tokenizer(bos_token='(BOS)'  ,eos_token='(EOS)'      )
							A						   =   self.get_image_processor(do_normalize=A_  ,padding_value=1.0      )

							A						   =   BlipProcessor.from_pretrained(
							    self.tmpdirname  ,bos_token='(BOS)'  ,eos_token='(EOS)'  ,do_normalize=A_  ,padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer  ,A_      )

							self.assertEqual(processor.image_processor.to_json_string()  ,image_processor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.image_processor  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[str]:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   self.prepare_image_inputs()

							A						   =   image_processor(A_  ,return_tensors='np'      )
							A						   =   processor(images=A_  ,return_tensors='np'      )

							for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1e-2      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[Any]:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   'lower newer'

							A						   =   processor(text=A_      )

							A						   =   tokenizer(A_  ,return_token_type_ids=A_      )

							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   'lower newer'
							A						   =   self.prepare_image_inputs()

							A						   =   processor(text=A_  ,images=A_      )

							self.assertListEqual(list(inputs.keys()      )  ,['pixel_values', 'input_ids', 'attention_mask']      )

							# test if it raises when no input is passed
							with pytest.raises(A_      ):
								processor()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A						   =   processor.batch_decode(A_      )
							A						   =   tokenizer.batch_decode(A_      )

							self.assertListEqual(A_  ,A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Any:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   'lower newer'
							A						   =   self.prepare_image_inputs()

							A						   =   processor(text=A_  ,images=A_      )

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()      )  ,['pixel_values', 'input_ids', 'attention_mask']      )

"""simple docstring"""


from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests






def       _snake_case				(		snake_case__   :     str = "isbn/0140328726"     ):
	A						   =   olid.strip().strip('/'     )  # Remove leading/trailing whitespace & slashes
	if new_olid.count('/'     ) != 1:
		A						   =   F'{olid} is not a valid Open Library olid'
		raise ValueError(snake_case__     )
	return requests.get(F'https://openlibrary.org/{new_olid}.json'     ).json()






def       _snake_case				(		snake_case__   :     dict     ):
	A						   =   {
	    'title': 'Title',
	    'publish_date': 'Publish date',
	    'authors': 'Authors',
	    'number_of_pages': 'Number of pages:',
	    'first_sentence': 'First sentence',
	    'isbn_10': 'ISBN (10)',
	    'isbn_13': 'ISBN (13)',
	}
	A						   =   {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
	A						   =   [
	    get_openlibrary_data(author['key']     )['name'] for author in data['Authors']
	]
	A						   =   data['First sentence']['value']
	for key, value in data.items():
		if isinstance(snake_case__	,      snake_case__     ):
			A						   =   ', '.join(snake_case__     )
	return data


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		while True:
				_lowercase							   =				input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
				if isbn.lower() in ("", "q", "quit", "exit", "stop"):
						break

				if len(isbn) not in (10, 13) or not isbn.isdigit():
						print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
						continue

				print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

				try:
						_lowercase							   =				summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
						print('''\n'''.join(F"""{key}: {value}""" for key, value in book_summary.items()))
				except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
						print(F"""Sorry, there are no results for ISBN: {isbn}.""")