


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
   import torch


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class      a    (		unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			List[Any]							       =       ViTImageProcessor if is_vision_available() else None

     @property
     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Any  							=      (3, 32, 128)
          __UpperCAmelCase     :			Tuple  							=      tempfile.mkdtemp()

          # fmt: off
          __UpperCAmelCase     :			Optional[int]  							=      ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
          # fmt: on
          __UpperCAmelCase     :			Optional[int]  							=      dict(zip(__a							,			range(len(__a      )      )      )      )

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

          __UpperCAmelCase     :			Union[str, Any]  							=      {
              'do_normalize': False,
              'do_resize': True,
              'image_processor_type': 'ViTImageProcessor',
              'resample': 3,
              'size': {'height': 32, 'width': 128},
          }
          __UpperCAmelCase     :			Dict  							=      os.path.join(self.tmpdirname							,			__a      )
          with open(self.image_processor_file							,			'''w'''							,			encoding='''utf-8'''      ) as fp:
               json.dump(__a							,			__a      )

     def 			lowerCamelCase__			(    self					:		str							,			**snake_case					:		Any      )						->							str:
          return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**__a      )

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			**snake_case					:		Any      )						->							Union[str, Any]:
          return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**__a      )

     def 			lowerCamelCase__			(    self					:		str      )						->							Dict:
          shutil.rmtree(self.tmpdirname      )

     def 			lowerCamelCase__			(    self					:		int      )						->							Dict:
          __UpperCAmelCase     :			List[Any]  							=      np.random.randint(255							,			size=(3, 30, 400)							,			dtype=np.uinta      )

          __UpperCAmelCase     :			Dict  							=      Image.fromarray(np.moveaxis(__a							,			0							,			-1      )      )

          return image_input

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Dict  							=      self.get_tokenizer()
          __UpperCAmelCase     :			List[str]  							=      self.get_image_processor()

          __UpperCAmelCase     :			Optional[int]  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )
          processor.save_pretrained(self.tmpdirname      )
          __UpperCAmelCase     :			Union[str, Any]  							=      MgpstrProcessor.from_pretrained(self.tmpdirname							,			use_fast=__a      )

          self.assertEqual(processor.char_tokenizer.get_vocab()							,			tokenizer.get_vocab()      )
          self.assertIsInstance(processor.char_tokenizer							,			__a      )

          self.assertEqual(processor.image_processor.to_json_string()							,			image_processor.to_json_string()      )
          self.assertIsInstance(processor.image_processor							,			__a      )

     def 			lowerCamelCase__			(    self					:		str      )						->							List[str]:
          __UpperCAmelCase     :			str  							=      self.get_tokenizer()
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_image_processor()

          __UpperCAmelCase     :			Optional[int]  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )
          processor.save_pretrained(self.tmpdirname      )

          __UpperCAmelCase     :			str  							=      self.get_tokenizer(bos_token='''(BOS)'''							,			eos_token='''(EOS)'''      )
          __UpperCAmelCase     :			str  							=      self.get_image_processor(do_normalize=__a							,			padding_value=1.0      )

          __UpperCAmelCase     :			Any  							=      MgpstrProcessor.from_pretrained(
              self.tmpdirname							,			bos_token='''(BOS)'''							,			eos_token='''(EOS)'''							,			do_normalize=__a							,			padding_value=1.0      )

          self.assertEqual(processor.char_tokenizer.get_vocab()							,			tokenizer_add_kwargs.get_vocab()      )
          self.assertIsInstance(processor.char_tokenizer							,			__a      )

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

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_image_processor()
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Optional[Any]  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.prepare_image_inputs()

          __UpperCAmelCase     :			Any  							=      image_processor(__a							,			return_tensors='''np'''      )
          __UpperCAmelCase     :			Optional[int]  							=      processor(images=__a							,			return_tensors='''np'''      )

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

     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			Dict  							=      self.get_image_processor()
          __UpperCAmelCase     :			List[Any]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			int  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )

          __UpperCAmelCase     :			Tuple  							=      'test'

          __UpperCAmelCase     :			Tuple  							=      processor(text=__a      )

          __UpperCAmelCase     :			Tuple  							=      tokenizer(__a      )
          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]      )

     def 			lowerCamelCase__			(    self					:		int      )						->							Dict:
          __UpperCAmelCase     :			Dict  							=      self.get_image_processor()
          __UpperCAmelCase     :			Optional[int]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Optional[Any]  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )

          __UpperCAmelCase     :			Dict  							=      'test'
          __UpperCAmelCase     :			Tuple  							=      self.prepare_image_inputs()

          __UpperCAmelCase     :			Optional[Any]  							=      processor(text=__a							,			images=__a      )

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

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

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      self.get_image_processor()
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Tuple  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )

          __UpperCAmelCase     :			Any  							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          __UpperCAmelCase     :			List[Any]  							=      processor.char_decode(__a      )
          __UpperCAmelCase     :			Any  							=      tokenizer.batch_decode(__a      )
          __UpperCAmelCase     :			List[Any]  							=      [seq.replace(''' '''							,			''''''      ) for seq in decoded_tok]

          self.assertListEqual(__a							,			__a      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      self.get_image_processor()
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Union[str, Any]  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )

          __UpperCAmelCase     :			Optional[int]  							=      None
          __UpperCAmelCase     :			List[Any]  							=      self.prepare_image_inputs()

          __UpperCAmelCase     :			Optional[Any]  							=      processor(text=__a							,			images=__a      )

          self.assertListEqual(list(inputs.keys()      )							,			processor.model_input_names      )






     def 			lowerCamelCase__			(    self					:		Any      )						->							Tuple:
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_image_processor()
          __UpperCAmelCase     :			int  							=      self.get_tokenizer()

          __UpperCAmelCase     :			Optional[int]  							=      MgpstrProcessor(tokenizer=__a							,			image_processor=__a      )

          __UpperCAmelCase     :			str  							=      torch.randn(1							,			27							,			38      )
          __UpperCAmelCase     :			int  							=      torch.randn(1							,			27							,			5_0257      )
          __UpperCAmelCase     :			Optional[int]  							=      torch.randn(1							,			27							,			3_0522      )

          __UpperCAmelCase     :			Tuple  							=      processor.batch_decode([char_input, bpe_input, wp_input]      )

          self.assertListEqual(list(results.keys()      )							,			['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']      )



'''simple docstring'''



import requests

__UpperCAmelCase      :Union[str, Any]			    =       "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      requests.get(_NEWS_API + bbc_news_api_key  ).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page['''articles''']      , 1  ):
          print(F'{i}.) {article["title"]}'  )


if __name__ == "__main__":
      fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")