



"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import numpy
 import tensorflow as tf

 from transformers import (
     TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
     TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
     TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
     BertConfig,
     DPRConfig,
     TFDPRContextEncoder,
     TFDPRQuestionEncoder,
     TFDPRReader,
 )






class       UpperCAmelCase_    :



      def __init__(	self	:      Dict	,      A	:      Dict	,      A	:      Any=1_3	,      A	:      Union[str, Any]=7	,      A	:      Optional[int]=True	,      A	:      str=True	,      A	:      List[Any]=True	,      A	:      Optional[int]=True	,      A	:      Dict=9_9	,      A	:      Tuple=3_2	,      A	:      int=2	,      A	:      Union[str, Any]=4	,      A	:      Tuple=3_7	,      A	:      List[Any]="gelu"	,      A	:      Any=0.1	,      A	:      Union[str, Any]=0.1	,      A	:      List[Any]=5_1_2	,      A	:      List[str]=1_6	,      A	:      Tuple=2	,      A	:      Optional[Any]=0.02	,      A	:      List[Any]=3	,      A	:      Tuple=4	,      A	:      Optional[Any]=None	,      A	:      Tuple=0	,      ):
           _UpperCAmelCase     :      int														=       parent
           _UpperCAmelCase     :      int														=       batch_size
           _UpperCAmelCase     :      Tuple														=       seq_length
           _UpperCAmelCase     :      Union[str, Any]														=       is_training
           _UpperCAmelCase     :      List[str]														=       use_input_mask
           _UpperCAmelCase     :      Dict														=       use_token_type_ids
           _UpperCAmelCase     :      List[Any]														=       use_labels
           _UpperCAmelCase     :      Dict														=       vocab_size
           _UpperCAmelCase     :      str														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      List[Any]														=       num_attention_heads
           _UpperCAmelCase     :      Optional[Any]														=       intermediate_size
           _UpperCAmelCase     :      Optional[Any]														=       hidden_act
           _UpperCAmelCase     :      int														=       hidden_dropout_prob
           _UpperCAmelCase     :      List[Any]														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Optional[Any]														=       max_position_embeddings
           _UpperCAmelCase     :      int														=       type_vocab_size
           _UpperCAmelCase     :      Dict														=       type_sequence_label_size
           _UpperCAmelCase     :      Dict														=       initializer_range
           _UpperCAmelCase     :      Union[str, Any]														=       num_labels
           _UpperCAmelCase     :      List[Any]														=       num_choices
           _UpperCAmelCase     :      Union[str, Any]														=       scope
           _UpperCAmelCase     :      int														=       projection_dim



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Any														=       ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)

           _UpperCAmelCase     :      Optional[int]														=       None
           if self.use_input_mask:
                # follow test_modeling_tf_ctrl.py
                _UpperCAmelCase     :      Optional[Any]														=       random_attention_mask([self.batch_size, self.seq_length]		)

           _UpperCAmelCase     :      List[str]														=       None
           if self.use_token_type_ids:
                _UpperCAmelCase     :      List[Any]														=       ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size		)

           _UpperCAmelCase     :      Dict														=       None
           _UpperCAmelCase     :      str														=       None
           _UpperCAmelCase     :      Dict														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Optional[int]														=       ids_tensor([self.batch_size]	,      self.type_sequence_label_size		)
                _UpperCAmelCase     :      Tuple														=       ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels		)
                _UpperCAmelCase     :      int														=       ids_tensor([self.batch_size]	,      self.num_choices		)

           _UpperCAmelCase     :      Optional[int]														=       BertConfig(
               vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      is_decoder=A	,      initializer_range=self.initializer_range	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       DPRConfig(projection_dim=self.projection_dim	,      **config.to_dict()		)

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



      def 		snake_case_   (	self	:      List[str]	,      A	:      Any	,      A	:      Tuple	,      A	:      Optional[int]	,      A	:      Optional[int]	,      A	:      str	,      A	:      Any	,      A	:      Any		):
           _UpperCAmelCase     :      List[str]														=       TFDPRContextEncoder(config=A		)
           _UpperCAmelCase     :      str														=       model(A	,      attention_mask=A	,      token_type_ids=A		)
           _UpperCAmelCase     :      Dict														=       model(A	,      token_type_ids=A		)
           _UpperCAmelCase     :      Dict														=       model(A		)
           self.parent.assertEqual(result.pooler_output.shape	,      (self.batch_size, self.projection_dim or self.hidden_size)		)



      def 		snake_case_   (	self	:      int	,      A	:      List[str]	,      A	:      Tuple	,      A	:      List[str]	,      A	:      Any	,      A	:      int	,      A	:      List[Any]	,      A	:      int		):
           _UpperCAmelCase     :      Optional[int]														=       TFDPRQuestionEncoder(config=A		)
           _UpperCAmelCase     :      int														=       model(A	,      attention_mask=A	,      token_type_ids=A		)
           _UpperCAmelCase     :      Optional[int]														=       model(A	,      token_type_ids=A		)
           _UpperCAmelCase     :      Optional[Any]														=       model(A		)
           self.parent.assertEqual(result.pooler_output.shape	,      (self.batch_size, self.projection_dim or self.hidden_size)		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      Union[str, Any]	,      A	:      str	,      A	:      Any	,      A	:      Any	,      A	:      List[str]	,      A	:      Any	,      A	:      int		):
           _UpperCAmelCase     :      List[Any]														=       TFDPRReader(config=A		)
           _UpperCAmelCase     :      Union[str, Any]														=       model(A	,      attention_mask=A		)

           self.parent.assertEqual(result.start_logits.shape	,      (self.batch_size, self.seq_length)		)
           self.parent.assertEqual(result.end_logits.shape	,      (self.batch_size, self.seq_length)		)
           self.parent.assertEqual(result.relevance_logits.shape	,      (self.batch_size,)		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       self.prepare_config_and_inputs()
           (
               (
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			
           )     :      List[Any]														=       config_and_inputs
           _UpperCAmelCase     :      str														=       {"input_ids": input_ids}
           return config, inputs_dict







@require_tf
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  List[str]						=					(
          (
              TFDPRContextEncoder,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )
          if is_tf_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

      __SCREAMING_SNAKE_CASE		:  List[str]						=					False
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					False
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					False
      __SCREAMING_SNAKE_CASE		:  int						=					False
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					False



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       TFDPRModelTester(self		)
           _UpperCAmelCase     :      str														=       ConfigTester(self	,      config_class=A	,      hidden_size=3_7		)



      def 		snake_case_   (	self	:      Tuple		):
           self.config_tester.run_common_tests()



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_context_encoder(*A		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_question_encoder(*A		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_reader(*A		)



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      List[Any]														=       TFDPRContextEncoder.from_pretrained(A		)
                self.assertIsNotNone(A		)

           for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Optional[int]														=       TFDPRContextEncoder.from_pretrained(A		)
                self.assertIsNotNone(A		)

           for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Any														=       TFDPRQuestionEncoder.from_pretrained(A		)
                self.assertIsNotNone(A		)

           for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Union[str, Any]														=       TFDPRReader.from_pretrained(A		)
                self.assertIsNotNone(A		)







@require_tf
class       UpperCAmelCase_    (							unittest.TestCase		):



      @slow
      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Dict														=       TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"		)

           _UpperCAmelCase     :      Union[str, Any]														=       tf.constant(
               [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]		)  # [CLS] hello, is my dog cute? [SEP]
           _UpperCAmelCase     :      Optional[Any]														=       model(A		)[0]  # embedding shape = (1, 768)
           # compare the actual values for a slice.
           _UpperCAmelCase     :      Union[str, Any]														=       tf.constant(
               [
                   [
                       0.03_236_253,
                       0.12_753_335,
                       0.16_818_509,
                       0.00_279_786,
                       0.3_896_933,
                       0.24_264_945,
                       0.2_178_971,
                       -0.02_335_227,
                       -0.08_481_959,
                       -0.14_324_117,
                   ]
               ]		)
           self.assertTrue(numpy.allclose(output[:, :1_0].numpy()	,      expected_slice.numpy()	,      atol=1e-4		)		)





"""simple docstring"""




import requests

_lowerCAmelCase							:   List[Any]				=				"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  None:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       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>")
