




'''simple docstring'''



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.
lowerCamelCase_			=						{'LayoutLMv2Config', 'LayoutLMv3Config'}







@is_pipeline_test
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
   lowerCamelCase_      =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

   if model_mapping is not None:
      lowerCamelCase_      =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
   if tf_model_mapping is not None:
      lowerCamelCase_      =						{
          config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
      }



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ZeroShotClassificationPipeline(
          model=__lowerCamelCase  ,				tokenizer=__lowerCamelCase  ,				candidate_labels=["polics", "health"]   )
      return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				classifier("Who are you voting for in 2020?"  ,				candidate_labels="politics"   )
      self.assertEqual(__lowerCamelCase  ,				{"sequence": ANY(__lowerCamelCase   ), "labels": [ANY(__lowerCamelCase   )], "scores": [ANY(__lowerCamelCase   )]}   )

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

      _SCREAMING_SNAKE_CASE	=				classifier("Who are you voting for in 2020?"  ,				candidate_labels=["politics"]   )
      self.assertEqual(__lowerCamelCase  ,				{"sequence": ANY(__lowerCamelCase   ), "labels": [ANY(__lowerCamelCase   )], "scores": [ANY(__lowerCamelCase   )]}   )

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

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

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

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

      with self.assertRaises(__lowerCamelCase   ):
         classifier(""  ,				candidate_labels="politics"   )

      with self.assertRaises(__lowerCamelCase   ):
         classifier(__lowerCamelCase  ,				candidate_labels="politics"   )

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

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

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

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

      self.run_entailment_id(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Pipeline   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				zero_shot_classifier.model.config
      _SCREAMING_SNAKE_CASE	=				config.labelaid
      _SCREAMING_SNAKE_CASE	=				zero_shot_classifier.entailment_id

      _SCREAMING_SNAKE_CASE	=				{"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
      self.assertEqual(zero_shot_classifier.entailment_id  ,				-1   )

      _SCREAMING_SNAKE_CASE	=				{"entailment": 0, "neutral": 1, "contradiction": 2}
      self.assertEqual(zero_shot_classifier.entailment_id  ,				0   )

      _SCREAMING_SNAKE_CASE	=				{"ENTAIL": 0, "NON-ENTAIL": 1}
      self.assertEqual(zero_shot_classifier.entailment_id  ,				0   )

      _SCREAMING_SNAKE_CASE	=				{"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
      self.assertEqual(zero_shot_classifier.entailment_id  ,				2   )

      _SCREAMING_SNAKE_CASE	=				original_labelaid
      self.assertEqual(__lowerCamelCase  ,				zero_shot_classifier.entailment_id   )



   @require_torch
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				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_0_0  ,				candidate_labels=["politics", "public health", "science"]   )



   @require_torch
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pipeline(
          "zero-shot-classification"  ,				model="sshleifer/tiny-distilbert-base-cased-distilled-squad"  ,				framework="pt"  ,				)
      _SCREAMING_SNAKE_CASE	=				zero_shot_classifier(
          "Who are you voting for in 2020?"  ,				candidate_labels=["politics", "public health", "science"]   )

      self.assertEqual(
          nested_simplify(__lowerCamelCase   )  ,				{
              "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 							lowerCAmelCase_      (   self :     List[Any]   ):

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

      self.assertEqual(
          nested_simplify(__lowerCamelCase   )  ,				{
              "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 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pipeline("zero-shot-classification"  ,				model="roberta-large-mnli"  ,				framework="pt"   )
      _SCREAMING_SNAKE_CASE	=				zero_shot_classifier(
          "Who are you voting for in 2020?"  ,				candidate_labels=["politics", "public health", "science"]   )

      self.assertEqual(
          nested_simplify(__lowerCamelCase   )  ,				{
              "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],
          }  ,				)
      _SCREAMING_SNAKE_CASE	=				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=__lowerCamelCase  ,				)
      self.assertEqual(
          nested_simplify(__lowerCamelCase   )  ,				{
              "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 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				pipeline("zero-shot-classification"  ,				model="roberta-large-mnli"  ,				framework="tf"   )
      _SCREAMING_SNAKE_CASE	=				zero_shot_classifier(
          "Who are you voting for in 2020?"  ,				candidate_labels=["politics", "public health", "science"]   )

      self.assertEqual(
          nested_simplify(__lowerCamelCase   )  ,				{
              "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],
          }  ,				)
      _SCREAMING_SNAKE_CASE	=				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=__lowerCamelCase  ,				)
      self.assertEqual(
          nested_simplify(__lowerCamelCase   )  ,				{
              "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],
          }  ,				)







'''simple docstring'''



import random
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			float	,					__A    :			bool = False  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				{i: [] for i in range(__A  )}

   # if probability is greater or equal than 1, then generate a complete graph
   if probability >= 1:
      return complete_graph(__A  )
   # if probability is lower or equal than 0, then return a graph without edges
   if probability <= 0:
      return graph

   # for each couple of nodes, add an edge from u to v
   # if the number randomly generated is greater than probability probability
   for i in range(__A  ):
      for j in range(i + 1	,					__A  ):
         if random.random() < probability:
            graph[i].append(__A  )
            if not directed:
               # if the graph is undirected, add an edge in from j to i, either
               graph[j].append(__A  )
   return graph
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			dict:
   return {
       i: [j for j in range(__A  ) if i != j] for i in range(__A  )
   }


if __name__ == "__main__":
 import doctest

 doctest.testmod()

