


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}






@is_pipeline_test
class _lowerCamelCase(		unittest.TestCase     ):
       lowercase_				:			List[Any]									=    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       lowercase_				:			str									=    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              lowercase_				:			List[Any]									=    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              lowercase_				:			List[str]									=    {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	ZeroShotClassificationPipeline(
               model=__UpperCamelCase,   tokenizer=__UpperCamelCase,   candidate_labels=['polics', 'health'])
           return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     Tuple:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	classifier('Who are you voting for in 2020?',   candidate_labels='politics')
           self.assertEqual(__UpperCamelCase,   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase)]})

           # No kwarg
           _lowercase :							Dict   	=	classifier('Who are you voting for in 2020?',   ['politics'])
           self.assertEqual(__UpperCamelCase,   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase)]})

           _lowercase :							List[Any]   	=	classifier('Who are you voting for in 2020?',   candidate_labels=['politics'])
           self.assertEqual(__UpperCamelCase,   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase)]})

           _lowercase :							List[Any]   	=	classifier('Who are you voting for in 2020?',   candidate_labels='politics, public health')
           self.assertEqual(
               __UpperCamelCase,   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)]})
           self.assertAlmostEqual(sum(nested_simplify(outputs['scores'])),   1.0)

           _lowercase :							List[str]   	=	classifier('Who are you voting for in 2020?',   candidate_labels=['politics', 'public health'])
           self.assertEqual(
               __UpperCamelCase,   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)]})
           self.assertAlmostEqual(sum(nested_simplify(outputs['scores'])),   1.0)

           _lowercase :							Any   	=	classifier(
               'Who are you voting for in 2020?',   candidate_labels='politics',   hypothesis_template='This text is about {}')
           self.assertEqual(__UpperCamelCase,   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase)]})

           # https://github.com/huggingface/transformers/issues/13846
           _lowercase :							str   	=	classifier(['I am happy'],   ['positive', 'negative'])
           self.assertEqual(
               __UpperCamelCase,   [
                   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)]}
                   for i in range(1)
               ],   )
           _lowercase :							List[str]   	=	classifier(['I am happy', 'I am sad'],   ['positive', 'negative'])
           self.assertEqual(
               __UpperCamelCase,   [
                   {'sequence': ANY(__UpperCamelCase), 'labels': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)], 'scores': [ANY(__UpperCamelCase), ANY(__UpperCamelCase)]}
                   for i in range(2)
               ],   )

           with self.assertRaises(__UpperCamelCase):
               classifier('',   candidate_labels='politics')

           with self.assertRaises(__UpperCamelCase):
               classifier(__UpperCamelCase,   candidate_labels='politics')

           with self.assertRaises(__UpperCamelCase):
               classifier('Who are you voting for in 2020?',   candidate_labels='')

           with self.assertRaises(__UpperCamelCase):
               classifier('Who are you voting for in 2020?',   candidate_labels=__UpperCamelCase)

           with self.assertRaises(__UpperCamelCase):
               classifier(
                   'Who are you voting for in 2020?',   candidate_labels='politics',   hypothesis_template='Not formatting template',   )

           with self.assertRaises(__UpperCamelCase):
               classifier(
                   'Who are you voting for in 2020?',   candidate_labels='politics',   hypothesis_template=__UpperCamelCase,   )

           self.run_entailment_id(__UpperCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	zero_shot_classifier.model.config
           _lowercase :							Optional[int]   	=	config.labelaid
           _lowercase :							str   	=	zero_shot_classifier.entailment_id

           _lowercase :							Union[str, Any]   	=	{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
           self.assertEqual(zero_shot_classifier.entailment_id,   -1)

           _lowercase :							List[str]   	=	{'entailment': 0, 'neutral': 1, 'contradiction': 2}
           self.assertEqual(zero_shot_classifier.entailment_id,   0)

           _lowercase :							int   	=	{'ENTAIL': 0, 'NON-ENTAIL': 1}
           self.assertEqual(zero_shot_classifier.entailment_id,   0)

           _lowercase :							int   	=	{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
           self.assertEqual(zero_shot_classifier.entailment_id,   2)

           _lowercase :							Optional[Any]   	=	original_labelaid
           self.assertEqual(__UpperCamelCase,   zero_shot_classifier.entailment_id)
       @require_torch
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	pipeline(
               'zero-shot-classification',   model='sshleifer/tiny-distilbert-base-cased-distilled-squad',   framework='pt',   )
           # There was a regression in 4.10 for this
           # Adding a test so we don't make the mistake again.
           # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
           zero_shot_classifier(
               'Who are you voting for in 2020?' * 1_00,   candidate_labels=['politics', 'public health', 'science'])
       @require_torch
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							List[str]   	=	pipeline(
               'zero-shot-classification',   model='sshleifer/tiny-distilbert-base-cased-distilled-squad',   framework='pt',   )
           _lowercase :							int   	=	zero_shot_classifier(
               'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

           self.assertEqual(
               nested_simplify(__UpperCamelCase),   {
                   'sequence': 'Who are you voting for in 2020?',
                   'labels': ['science', 'public health', 'politics'],
                   'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
               },   )
       @require_tf
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Tuple   	=	pipeline(
               'zero-shot-classification',   model='sshleifer/tiny-distilbert-base-cased-distilled-squad',   framework='tf',   )
           _lowercase :							Any   	=	zero_shot_classifier(
               'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

           self.assertEqual(
               nested_simplify(__UpperCamelCase),   {
                   'sequence': 'Who are you voting for in 2020?',
                   'labels': ['science', 'public health', 'politics'],
                   'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
               },   )
       @slow
       @require_torch
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							List[Any]   	=	pipeline('zero-shot-classification',   model='roberta-large-mnli',   framework='pt')
           _lowercase :							Tuple   	=	zero_shot_classifier(
               'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

           self.assertEqual(
               nested_simplify(__UpperCamelCase),   {
                   'sequence': 'Who are you voting for in 2020?',
                   'labels': ['politics', 'public health', 'science'],
                   'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
               },   )
           _lowercase :							Dict   	=	zero_shot_classifier(
               'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
               ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
               ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
               ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
               ' machine translation tasks show these models to be superior in quality while being more parallelizable'
               ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
               ' English-to-German translation task, improving over the existing best results, including ensembles by'
               ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
               ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
               ' fraction of the training costs of the best models from the literature. We show that the Transformer'
               ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
               ' large and limited training data.',   candidate_labels=['machine learning', 'statistics', 'translation', 'vision'],   multi_label=__UpperCamelCase,   )
           self.assertEqual(
               nested_simplify(__UpperCamelCase),   {
                   'sequence': (
                       'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                       ' networks in an encoder-decoder configuration. The best performing models also connect the'
                       ' encoder and decoder through an attention mechanism. We propose a new simple network'
                       ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                       ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                       ' superior in quality while being more parallelizable and requiring significantly less time to'
                       ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                       ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                       ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                       ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                       ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                       ' other tasks by applying it successfully to English constituency parsing both with large and'
                       ' limited training data.'
                   ),
                   'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                   'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
               },   )




       @slow
       @require_tf
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	pipeline('zero-shot-classification',   model='roberta-large-mnli',   framework='tf')
           _lowercase :							Dict   	=	zero_shot_classifier(
               'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

           self.assertEqual(
               nested_simplify(__UpperCamelCase),   {
                   'sequence': 'Who are you voting for in 2020?',
                   'labels': ['politics', 'public health', 'science'],
                   'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
               },   )
           _lowercase :							Optional[int]   	=	zero_shot_classifier(
               'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
               ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
               ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
               ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
               ' machine translation tasks show these models to be superior in quality while being more parallelizable'
               ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
               ' English-to-German translation task, improving over the existing best results, including ensembles by'
               ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
               ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
               ' fraction of the training costs of the best models from the literature. We show that the Transformer'
               ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
               ' large and limited training data.',   candidate_labels=['machine learning', 'statistics', 'translation', 'vision'],   multi_label=__UpperCamelCase,   )
           self.assertEqual(
               nested_simplify(__UpperCamelCase),   {
                   'sequence': (
                       'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                       ' networks in an encoder-decoder configuration. The best performing models also connect the'
                       ' encoder and decoder through an attention mechanism. We propose a new simple network'
                       ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                       ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                       ' superior in quality while being more parallelizable and requiring significantly less time to'
                       ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                       ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                       ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                       ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                       ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                       ' other tasks by applying it successfully to English constituency parsing both with large and'
                       ' limited training data.'
                   ),
                   'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                   'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
               },   )






from __future__ import annotations

import requests



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict:
    _lowercase :							Dict   	=	F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
    return requests.get(lowerCamelCase_				).json()



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		list[dict]:
    _lowercase :							Dict   	=	'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
    _lowercase :							Dict   	=	requests.get(lowerCamelCase_				).json()[:max_stories]
    return [get_hackernews_story(lowerCamelCase_				) for story_id in story_ids]



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		str:
    _lowercase :							Optional[int]   	=	hackernews_top_stories(lowerCamelCase_				)
    return "\n".join('* [{title}]({url})'.format(**lowerCamelCase_				) for story in stories				)


if __name__ == "__main__":
 print(hackernews_top_stories_as_markdown())


