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					:	Optional[int]       =  {"""LayoutLMv2Config""", """LayoutLMv3Config"""}






@is_pipeline_test
class 		a__						(					unittest.TestCase					):
					A	       =							MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
					A	       =							TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       int,_A      :       Tuple,_A      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    ZeroShotClassificationPipeline(
							    model=_A,tokenizer=_A,candidate_labels=["polics", "health"]    )
							return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       Union[str, Any]    ):







							"""simple docstring"""






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

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

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

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

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

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

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

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

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

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

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

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

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

							self.run_entailment_id(_A    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Pipeline    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    zero_shot_classifier.model.config
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    config.labelaid
							SCREAMING_SNAKE_CASE_  :		Dict				      =    zero_shot_classifier.entailment_id

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
							self.assertEqual(zero_shot_classifier.entailment_id,-1    )

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

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"ENTAIL": 0, "NON-ENTAIL": 1}
							self.assertEqual(zero_shot_classifier.entailment_id,0    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
							self.assertEqual(zero_shot_classifier.entailment_id,2    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    original_labelaid
							self.assertEqual(_A,zero_shot_classifier.entailment_id    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, 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?" * 100,candidate_labels=["politics", "public health", "science"]    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






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

							self.assertEqual(
							    nested_simplify(_A    ),{
							        "sequence": "Who are you voting for in 2020?",
							        "labels": ["science", "public health", "politics"],
							        "scores": [0.333, 0.333, 0.333],
							    },)

					@require_tf
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






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

							self.assertEqual(
							    nested_simplify(_A    ),{
							        "sequence": "Who are you voting for in 2020?",
							        "labels": ["science", "public health", "politics"],
							        "scores": [0.333, 0.333, 0.333],
							    },)

					@slow
					@require_torch
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






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

							self.assertEqual(
							    nested_simplify(_A    ),{
							        "sequence": "Who are you voting for in 2020?",
							        "labels": ["politics", "public health", "science"],
							        "scores": [0.976, 0.015, 0.009],
							    },)
							SCREAMING_SNAKE_CASE_  :		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=_A,)
							self.assertEqual(
							    nested_simplify(_A    ),{
							        "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": [0.817, 0.713, 0.018, 0.018],
							    },)






					@slow
					@require_tf
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






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

							self.assertEqual(
							    nested_simplify(_A    ),{
							        "sequence": "Who are you voting for in 2020?",
							        "labels": ["politics", "public health", "science"],
							        "scores": [0.976, 0.015, 0.009],
							    },)
							SCREAMING_SNAKE_CASE_  :		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=_A,)
							self.assertEqual(
							    nested_simplify(_A    ),{
							        "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": [0.817, 0.713, 0.018, 0.018],
							    },)

'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      list    )    ->    list:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    len(_UpperCAmelCase    )
       for _ in range(_UpperCAmelCase    ):
              for i in range(_ % 2			,	arr_size - 1			,	2    ):
                     if arr[i + 1] < arr[i]:
                            _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  list(range(10, 0, -1))
  print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')

