





"""simple docstring"""



import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.generation import (
           FlaxForcedBOSTokenLogitsProcessor,
           FlaxForcedEOSTokenLogitsProcessor,
           FlaxLogitsProcessorList,
           FlaxMinLengthLogitsProcessor,
           FlaxTemperatureLogitsWarper,
           FlaxTopKLogitsWarper,
           FlaxTopPLogitsWarper,
       )






@require_flax
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	jnp.ones((batch_size, length)    ) / length
     return scores

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	None
     __A       :		Optional[int]							=	20

     __A       :		Dict							=	self._get_uniform_logits(batch_size=2   ,		length=__lowerCamelCase    )

     # tweak scores to not be uniform anymore
     __A       :		List[Any]							=	scores.at[1, 5].set((1 / length) + 0.1    )  # peak, 1st batch
     __A       :		Dict							=	scores.at[1, 10].set((1 / length) - 0.4    )  # valley, 1st batch

     # compute softmax
     __A       :		int							=	jax.nn.softmax(__lowerCamelCase   ,		axis=-1    )

     __A       :		List[str]							=	FlaxTemperatureLogitsWarper(temperature=0.5    )
     __A       :		int							=	FlaxTemperatureLogitsWarper(temperature=1.3    )

     __A       :		int							=	jax.nn.softmax(temp_dist_warper_sharper(__lowerCamelCase   ,		scores.copy()   ,		cur_len=__lowerCamelCase    )   ,		axis=-1    )
     __A       :		Optional[Any]							=	jax.nn.softmax(temp_dist_warper_smoother(__lowerCamelCase   ,		scores.copy()   ,		cur_len=__lowerCamelCase    )   ,		axis=-1    )

     # uniform distribution stays uniform
     self.assertTrue(jnp.allclose(probs[0, :]   ,		warped_prob_sharp[0, :]   ,		atol=1e-3    )    )
     self.assertTrue(jnp.allclose(probs[0, :]   ,		warped_prob_smooth[0, :]   ,		atol=1e-3    )    )

     # sharp peaks get higher, valleys get lower
     self.assertLess(probs[1, :].max()   ,		warped_prob_sharp[1, :].max()    )
     self.assertGreater(probs[1, :].min()   ,		warped_prob_sharp[1, :].min()    )

     # smooth peaks get lower, valleys get higher
     self.assertGreater(probs[1, :].max()   ,		warped_prob_smooth[1, :].max()    )
     self.assertLess(probs[1, :].min()   ,		warped_prob_smooth[1, :].min()    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	None
     __A       :		Optional[Any]							=	10
     __A       :		Dict							=	2

     # create ramp distribution
     __A       :		Dict							=	np.broadcast_to(np.arange(__lowerCamelCase    )[None, :]   ,		(batch_size, vocab_size)    ).copy()
     __A       :		str							=	ramp_logits[1:, : vocab_size // 2] + vocab_size

     __A       :		Optional[int]							=	FlaxTopKLogitsWarper(3    )

     __A       :		Optional[int]							=	top_k_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )

     # check that correct tokens are filtered
     self.assertListEqual(jnp.isinf(scores[0]    ).tolist()   ,		7 * [True] + 3 * [False]    )
     self.assertListEqual(jnp.isinf(scores[1]    ).tolist()   ,		2 * [True] + 3 * [False] + 5 * [True]    )

     # check special case
     __A       :		Dict							=	5
     __A       :		int							=	FlaxTopKLogitsWarper(top_k=1   ,		filter_value=0.0   ,		min_tokens_to_keep=3    )

     __A       :		Tuple							=	np.broadcast_to(np.arange(__lowerCamelCase    )[None, :]   ,		(batch_size, length)    ).copy()
     __A       :		List[Any]							=	top_k_warp_safety_check(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )

     # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
     self.assertListEqual((scores == 0.0).sum(axis=-1    ).tolist()   ,		[2, 2]    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	None
     __A       :		List[str]							=	10
     __A       :		Optional[int]							=	2

     # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
     __A       :		Any							=	np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]    )    )

     __A       :		Union[str, Any]							=	FlaxTopPLogitsWarper(0.8    )
     __A       :		Any							=	np.exp(top_p_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )    )

     # dist should be filtered to keep min num values so that sum is >= top_p
     # exp (-inf) => 0
     __A       :		str							=	np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]    )
     self.assertTrue(np.allclose(__lowerCamelCase   ,		__lowerCamelCase   ,		atol=1e-3    )    )

     # check edge cases with negative and extreme logits
     __A       :		List[Any]							=	np.broadcast_to(np.arange(__lowerCamelCase    )[None, :]   ,		(batch_size, vocab_size)    ).copy() - (
         vocab_size // 2
     )

     # make ramp_logits more extreme
     __A       :		Tuple							=	ramp_logits[1] * 1_0_0.0

     # make sure at least 2 tokens are kept
     __A       :		Tuple							=	FlaxTopPLogitsWarper(0.9   ,		min_tokens_to_keep=2   ,		filter_value=0.0    )
     __A       :		Optional[Any]							=	top_p_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )

     # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
     self.assertListEqual((filtered_dist != 0.0).sum(axis=-1    ).tolist()   ,		[3, 2]    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	20
     __A       :		List[str]							=	4
     __A       :		Optional[int]							=	0

     __A       :		str							=	FlaxMinLengthLogitsProcessor(min_length=10   ,		eos_token_id=__lowerCamelCase    )

     # check that min length is applied at length 5
     __A       :		Tuple							=	ids_tensor((batch_size, 20)   ,		vocab_size=20    )
     __A       :		Optional[int]							=	5
     __A       :		Dict							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Union[str, Any]							=	min_dist_processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,		4 * [-float('''inf'''    )]    )

     # check that min length is not applied anymore at length 15
     __A       :		List[Any]							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Dict							=	15
     __A       :		List[Any]							=	min_dist_processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     self.assertFalse(jnp.isinf(__lowerCamelCase    ).any()    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	20
     __A       :		Any							=	4
     __A       :		Any							=	0

     __A       :		Dict							=	FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCamelCase    )

     # check that all scores are -inf except the bos_token_id score
     __A       :		Union[str, Any]							=	ids_tensor((batch_size, 1)   ,		vocab_size=20    )
     __A       :		List[Any]							=	1
     __A       :		Union[str, Any]							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Dict							=	logits_processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]    ).all()    )
     self.assertListEqual(scores[:, bos_token_id].tolist()   ,		4 * [0]    )  # score for bos_token_id shold be zero

     # check that bos_token_id is not forced if current length is greater than 1
     __A       :		Optional[Any]							=	3
     __A       :		int							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Optional[Any]							=	logits_processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     self.assertFalse(jnp.isinf(__lowerCamelCase    ).any()    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	20
     __A       :		Tuple							=	4
     __A       :		Optional[int]							=	0
     __A       :		int							=	5

     __A       :		Union[str, Any]							=	FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCamelCase   ,		eos_token_id=__lowerCamelCase    )

     # check that all scores are -inf except the eos_token_id when max_length is reached
     __A       :		List[str]							=	ids_tensor((batch_size, 4)   ,		vocab_size=20    )
     __A       :		Optional[int]							=	4
     __A       :		Tuple							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Tuple							=	logits_processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]    ).all()    )
     self.assertListEqual(scores[:, eos_token_id].tolist()   ,		4 * [0]    )  # score for eos_token_id should be zero

     # check that eos_token_id is not forced if max_length is not reached
     __A       :		Dict							=	3
     __A       :		Tuple							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Tuple							=	logits_processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     self.assertFalse(jnp.isinf(__lowerCamelCase    ).any()    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	4
     __A       :		int							=	10
     __A       :		Optional[int]							=	15
     __A       :		Tuple							=	2
     __A       :		List[str]							=	1
     __A       :		Tuple							=	15

     # dummy input_ids and scores
     __A       :		List[str]							=	ids_tensor((batch_size, sequence_length)   ,		__lowerCamelCase    )
     __A       :		int							=	input_ids.copy()

     __A       :		int							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Optional[Any]							=	scores.copy()

     # instantiate all dist processors
     __A       :		List[Any]							=	FlaxTemperatureLogitsWarper(temperature=0.5    )
     __A       :		Any							=	FlaxTopKLogitsWarper(3    )
     __A       :		Optional[Any]							=	FlaxTopPLogitsWarper(0.8    )

     # instantiate all logits processors
     __A       :		Any							=	FlaxMinLengthLogitsProcessor(min_length=10   ,		eos_token_id=__lowerCamelCase    )
     __A       :		Optional[Any]							=	FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCamelCase    )
     __A       :		Dict							=	FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCamelCase   ,		eos_token_id=__lowerCamelCase    )

     __A       :		Dict							=	10

     # no processor list
     __A       :		Tuple							=	temp_dist_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     __A       :		Optional[int]							=	top_k_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     __A       :		Optional[Any]							=	top_p_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     __A       :		Tuple							=	min_dist_proc(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     __A       :		List[Any]							=	bos_dist_proc(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
     __A       :		List[Any]							=	eos_dist_proc(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )

     # with processor list
     __A       :		str							=	FlaxLogitsProcessorList(
         [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]    )
     __A       :		str							=	processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )

     # scores should be equal
     self.assertTrue(jnp.allclose(__lowerCamelCase   ,		__lowerCamelCase   ,		atol=1e-3    )    )

     # input_ids should never be changed
     self.assertListEqual(input_ids.tolist()   ,		input_ids_comp.tolist()    )







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	4
     __A       :		Tuple							=	10
     __A       :		Dict							=	15
     __A       :		Optional[Any]							=	2
     __A       :		Dict							=	1
     __A       :		Any							=	15

     # dummy input_ids and scores
     __A       :		List[str]							=	ids_tensor((batch_size, sequence_length)   ,		__lowerCamelCase    )
     __A       :		Dict							=	input_ids.copy()

     __A       :		Union[str, Any]							=	self._get_uniform_logits(__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		int							=	scores.copy()

     # instantiate all dist processors
     __A       :		Tuple							=	FlaxTemperatureLogitsWarper(temperature=0.5    )
     __A       :		int							=	FlaxTopKLogitsWarper(3    )
     __A       :		Optional[int]							=	FlaxTopPLogitsWarper(0.8    )

     # instantiate all logits processors
     __A       :		int							=	FlaxMinLengthLogitsProcessor(min_length=10   ,		eos_token_id=__lowerCamelCase    )
     __A       :		Dict							=	FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCamelCase    )
     __A       :		Optional[int]							=	FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCamelCase   ,		eos_token_id=__lowerCamelCase    )

     __A       :		Dict							=	10

     # no processor list
     def run_no_processor_list(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	temp_dist_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         __A       :		Optional[Any]							=	top_k_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         __A       :		Any							=	top_p_warp(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         __A       :		Dict							=	min_dist_proc(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         __A       :		int							=	bos_dist_proc(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         __A       :		List[Any]							=	eos_dist_proc(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         return scores

     # with processor list
     def run_processor_list(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		str							=	FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]    )
         __A       :		Dict							=	processor(__lowerCamelCase   ,		__lowerCamelCase   ,		cur_len=__lowerCamelCase    )
         return scores

     __A       :		Tuple							=	jax.jit(__lowerCamelCase    )
     __A       :		List[str]							=	jax.jit(__lowerCamelCase    )

     __A       :		List[str]							=	jitted_run_no_processor_list(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Tuple							=	jitted_run_processor_list(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )

     # scores should be equal
     self.assertTrue(jnp.allclose(__lowerCamelCase   ,		__lowerCamelCase   ,		atol=1e-3    )    )

     # input_ids should never be changed
     self.assertListEqual(input_ids.tolist()   ,		input_ids_comp.tolist()    )











"""simple docstring"""



import requests

a_					=     """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		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>""")




