



"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _lowerCAmelCase							:   List[Any]				=				"platform"

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		Optional[Any]=None		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]=None		,    SCREAMING_SNAKE_CASE__      :		List[Any]=None		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		List[str]=None		,    SCREAMING_SNAKE_CASE__      :		Dict=None		,    )    ->  Dict:





     '''simple docstring'''

     if attention_mask is None:
          _UpperCAmelCase     :      List[str]														=       np.where(input_ids != config.pad_token_id		,    1		,    0							)
     if decoder_attention_mask is None:
          _UpperCAmelCase     :      Optional[Any]														=       np.where(decoder_input_ids != config.pad_token_id		,    1		,    0							)
     if head_mask is None:
          _UpperCAmelCase     :      str														=       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          _UpperCAmelCase     :      Optional[Any]														=       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     if cross_attn_head_mask is None:
          _UpperCAmelCase     :      int														=       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": attention_mask,
     }






class       UpperCAmelCase_    :



      def __init__(	self	:      Union[str, Any]	,      A	:      Tuple	,      A	:      Optional[Any]=1_3	,      A	:      Union[str, Any]=7	,      A	:      Union[str, Any]=True	,      A	:      Tuple=False	,      A	:      Any=9_9	,      A	:      List[Any]=1_6	,      A	:      Dict=2	,      A	:      Union[str, Any]=4	,      A	:      Optional[Any]=4	,      A	:      Optional[Any]="gelu"	,      A	:      Union[str, Any]=0.1	,      A	:      List[Any]=0.1	,      A	:      List[str]=3_2	,      A	:      Dict=2	,      A	:      Optional[Any]=1	,      A	:      Optional[Any]=0	,      A	:      Any=0.02	,      ):
           _UpperCAmelCase     :      int														=       parent
           _UpperCAmelCase     :      str														=       batch_size
           _UpperCAmelCase     :      Optional[int]														=       seq_length
           _UpperCAmelCase     :      List[Any]														=       is_training
           _UpperCAmelCase     :      Union[str, Any]														=       use_labels
           _UpperCAmelCase     :      int														=       vocab_size
           _UpperCAmelCase     :      List[str]														=       hidden_size
           _UpperCAmelCase     :      Union[str, Any]														=       num_hidden_layers
           _UpperCAmelCase     :      List[Any]														=       num_attention_heads
           _UpperCAmelCase     :      Dict														=       intermediate_size
           _UpperCAmelCase     :      Optional[int]														=       hidden_act
           _UpperCAmelCase     :      List[Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      Union[str, Any]														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Any														=       max_position_embeddings
           _UpperCAmelCase     :      List[str]														=       eos_token_id
           _UpperCAmelCase     :      Optional[int]														=       pad_token_id
           _UpperCAmelCase     :      str														=       bos_token_id
           _UpperCAmelCase     :      Optional[Any]														=       initializer_range



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Union[str, Any]														=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,      self.vocab_size		)	,      3	,      self.vocab_size		)
           _UpperCAmelCase     :      Optional[int]														=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,      dtype=np.intaa		))	,      -1		)

           _UpperCAmelCase     :      int														=       shift_tokens_right(A	,      1	,      2		)

           _UpperCAmelCase     :      Tuple														=       BlenderbotConfig(
               vocab_size=self.vocab_size	,      d_model=self.hidden_size	,      encoder_layers=self.num_hidden_layers	,      decoder_layers=self.num_hidden_layers	,      encoder_attention_heads=self.num_attention_heads	,      decoder_attention_heads=self.num_attention_heads	,      encoder_ffn_dim=self.intermediate_size	,      decoder_ffn_dim=self.intermediate_size	,      dropout=self.hidden_dropout_prob	,      attention_dropout=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      eos_token_id=self.eos_token_id	,      bos_token_id=self.bos_token_id	,      pad_token_id=self.pad_token_id	,      initializer_range=self.initializer_range	,      use_cache=A	,      )
           _UpperCAmelCase     :      Any														=       prepare_blenderbot_inputs_dict(A	,      A	,      A		)
           return config, inputs_dict



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[int]														=       self.prepare_config_and_inputs()
           return config, inputs_dict



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Dict	,      A	:      Union[str, Any]	,      A	:      str		):
           _UpperCAmelCase     :      Union[str, Any]														=       2_0
           _UpperCAmelCase     :      Any														=       model_class_name(A		)

           _UpperCAmelCase     :      Dict														=       model.encode(inputs_dict["input_ids"]		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           _UpperCAmelCase     :      Optional[Any]														=       model.init_cache(decoder_input_ids.shape[0]	,      A	,      A		)
           _UpperCAmelCase     :      Optional[int]														=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,      dtype="i4"		)

           _UpperCAmelCase     :      Any														=       jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]	,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,      )
           _UpperCAmelCase     :      Optional[Any]														=       model.decode(
               decoder_input_ids[:, :-1]	,      A	,      decoder_attention_mask=A	,      past_key_values=A	,      decoder_position_ids=A	,      )

           _UpperCAmelCase     :      Any														=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,      dtype="i4"		)
           _UpperCAmelCase     :      Optional[Any]														=       model.decode(
               decoder_input_ids[:, -1:]	,      A	,      decoder_attention_mask=A	,      past_key_values=outputs_cache.past_key_values	,      decoder_position_ids=A	,      )

           _UpperCAmelCase     :      int														=       model.decode(A	,      A		)

           _UpperCAmelCase     :      Optional[Any]														=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
           self.parent.assertTrue(diff < 1e-3	,      msg=f'Max diff is {diff}'		)



      def 		snake_case_   (	self	:      Any	,      A	:      Tuple	,      A	:      List[str]	,      A	:      List[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       2_0
           _UpperCAmelCase     :      Union[str, Any]														=       model_class_name(A		)

           _UpperCAmelCase     :      List[Any]														=       model.encode(inputs_dict["input_ids"]		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           _UpperCAmelCase     :      List[Any]														=       jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
               ]	,      axis=-1	,      )

           _UpperCAmelCase     :      Dict														=       model.init_cache(decoder_input_ids.shape[0]	,      A	,      A		)
           _UpperCAmelCase     :      Optional[int]														=       jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]	,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,      )

           _UpperCAmelCase     :      List[str]														=       model.decode(
               decoder_input_ids[:, :-1]	,      A	,      decoder_attention_mask=A	,      past_key_values=A	,      decoder_position_ids=A	,      )
           _UpperCAmelCase     :      Tuple														=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,      dtype="i4"		)
           _UpperCAmelCase     :      Optional[int]														=       model.decode(
               decoder_input_ids[:, -1:]	,      A	,      past_key_values=outputs_cache.past_key_values	,      decoder_attention_mask=A	,      decoder_position_ids=A	,      )

           _UpperCAmelCase     :      int														=       model.decode(A	,      A	,      decoder_attention_mask=A		)

           _UpperCAmelCase     :      Tuple														=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
           self.parent.assertTrue(diff < 1e-3	,      msg=f'Max diff is {diff}'		)







@require_flax
class       UpperCAmelCase_    (							unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					9_9



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Any														=       np.array(
               [
                   [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                   [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                   [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                   [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                   [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                   [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                   [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                   [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                   [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                   [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                   [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                   [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                   [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
               ]	,      dtype=np.intaa	,      )

           _UpperCAmelCase     :      int														=       input_ids.shape[0]
           _UpperCAmelCase     :      Any														=       BlenderbotConfig(
               vocab_size=self.vocab_size	,      d_model=2_4	,      encoder_layers=2	,      decoder_layers=2	,      encoder_attention_heads=2	,      decoder_attention_heads=2	,      encoder_ffn_dim=3_2	,      decoder_ffn_dim=3_2	,      max_position_embeddings=4_8	,      eos_token_id=2	,      pad_token_id=1	,      bos_token_id=0	,      )
           return config, input_ids, batch_size



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      str														=       self._get_config_and_data()
           _UpperCAmelCase     :      str														=       FlaxBlenderbotForConditionalGeneration(A		)
           _UpperCAmelCase     :      int														=       lm_model(input_ids=A		)
           _UpperCAmelCase     :      Optional[int]														=       (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["logits"].shape	,      A		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Dict														=       BlenderbotConfig(
               vocab_size=self.vocab_size	,      d_model=1_4	,      encoder_layers=2	,      decoder_layers=2	,      encoder_attention_heads=2	,      decoder_attention_heads=2	,      encoder_ffn_dim=8	,      decoder_ffn_dim=8	,      max_position_embeddings=4_8	,      )
           _UpperCAmelCase     :      Tuple														=       FlaxBlenderbotForConditionalGeneration(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]	,      dtype=np.intaa		)
           _UpperCAmelCase     :      Union[str, Any]														=       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]	,      dtype=np.intaa		)
           _UpperCAmelCase     :      Optional[int]														=       lm_model(input_ids=A	,      decoder_input_ids=A		)
           _UpperCAmelCase     :      Optional[Any]														=       (*summary.shape, config.vocab_size)
           self.assertEqual(outputs["logits"].shape	,      A		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      str														=       np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]	,      dtype=np.intaa		)
           _UpperCAmelCase     :      Union[str, Any]														=       shift_tokens_right(A	,      1	,      2		)
           _UpperCAmelCase     :      Any														=       np.equal(A	,      1		).astype(np.floataa		).sum()
           _UpperCAmelCase     :      Optional[int]														=       np.equal(A	,      1		).astype(np.floataa		).sum()
           self.assertEqual(shifted.shape	,      input_ids.shape		)
           self.assertEqual(A	,      n_pad_before - 1		)
           self.assertTrue(np.equal(shifted[:, 0]	,      2		).all()		)







@require_flax
class       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase			,     _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					True
      __SCREAMING_SNAKE_CASE		:  Any						=					(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:  Any						=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       FlaxBlenderbotModelTester(self		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(A	,      A	,      A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward_with_attn_mask(A	,      A	,      A		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__		):
                     _UpperCAmelCase     :      List[str]														=       self._prepare_for_class(A	,      A		)
                     _UpperCAmelCase     :      Dict														=       model_class(A		)

                     @jax.jit
                     def encode_jitted(A	:      Tuple	,      A	:      Optional[Any]=None	,      **A	:      Dict		):
                          return model.encode(input_ids=A	,      attention_mask=A		)

                     with self.subTest("JIT Enabled"		):
                          _UpperCAmelCase     :      Tuple														=       encode_jitted(**A		).to_tuple()

                     with self.subTest("JIT Disabled"		):
                          with jax.disable_jit():
                               _UpperCAmelCase     :      int														=       encode_jitted(**A		).to_tuple()

                     self.assertEqual(len(A		)	,      len(A		)		)
                     for jitted_output, output in zip(A	,      A		):
                          self.assertEqual(jitted_output.shape	,      output.shape		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__		):
                     _UpperCAmelCase     :      Optional[int]														=       model_class(A		)
                     _UpperCAmelCase     :      Any														=       model.encode(inputs_dict["input_ids"]	,      inputs_dict["attention_mask"]		)

                     _UpperCAmelCase     :      Any														=       {
                         "decoder_input_ids": inputs_dict["decoder_input_ids"],
                         "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                         "encoder_outputs": encoder_outputs,
                     }

                     @jax.jit
                     def decode_jitted(A	:      int	,      A	:      Tuple	,      A	:      Optional[int]		):
                          return model.decode(
                              decoder_input_ids=A	,      decoder_attention_mask=A	,      encoder_outputs=A	,      )

                     with self.subTest("JIT Enabled"		):
                          _UpperCAmelCase     :      Any														=       decode_jitted(**A		).to_tuple()

                     with self.subTest("JIT Disabled"		):
                          with jax.disable_jit():
                               _UpperCAmelCase     :      Optional[int]														=       decode_jitted(**A		).to_tuple()

                     self.assertEqual(len(A		)	,      len(A		)		)
                     for jitted_output, output in zip(A	,      A		):
                          self.assertEqual(jitted_output.shape	,      output.shape		)



      @slow
      def 		snake_case_   (	self	:      Any		):
           for model_class_name in self.all_model_classes:
                _UpperCAmelCase     :      int														=       model_class_name.from_pretrained("facebook/blenderbot-400M-distill"		)
                # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                _UpperCAmelCase     :      Union[str, Any]														=       np.ones((1, 1)		) * model.config.eos_token_id
                _UpperCAmelCase     :      str														=       model(A		)
                self.assertIsNotNone(A		)



      @unittest.skipUnless(jax_device != "cpu"	,      "3B test too slow on CPU."		)
      @slow
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
           _UpperCAmelCase     :      Any														=       {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

           _UpperCAmelCase     :      Any														=       FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"	,      from_pt=A		)
           _UpperCAmelCase     :      Tuple														=       BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"		)

           _UpperCAmelCase     :      int														=       ["Sam"]
           _UpperCAmelCase     :      Any														=       tokenizer(A	,      return_tensors="jax"		)

           _UpperCAmelCase     :      Optional[int]														=       model.generate(**A	,      **A		)
           _UpperCAmelCase     :      Dict														=       "Sam is a great name. It means \"sun\" in Gaelic."

           _UpperCAmelCase     :      int														=       tokenizer.batch_decode(A	,      **A		)
           assert generated_txt[0].strip() == tgt_text





"""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>")
