

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.
a		={"""LayoutLMv2Config""", """LayoutLMv3Config"""}






@is_pipeline_test
class 	A_		(	unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							_UpperCAmelCase   :  Union[str, Any]										=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														_UpperCAmelCase   :  Tuple										=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														_UpperCAmelCase   :  Any										=				{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Optional[Any] 	=			ZeroShotClassificationPipeline(
								    model=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__       ,candidate_labels=['polics', 'health'])
								return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								__lowerCamelCase    :    List[str] 	=			classifier('Who are you voting for in 2020?'       ,candidate_labels='politics')
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,{'sequence': ANY(SCREAMING_SNAKE_CASE__), 'labels': [ANY(SCREAMING_SNAKE_CASE__)], 'scores': [ANY(SCREAMING_SNAKE_CASE__)]})

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

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

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

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

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

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

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

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

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

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

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

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

								self.run_entailment_id(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Pipeline):
								__lowerCamelCase    :    Optional[Any] 	=			zero_shot_classifier.model.config
								__lowerCamelCase    :    List[str] 	=			config.labelaid
								__lowerCamelCase    :    Optional[int] 	=			zero_shot_classifier.entailment_id

								__lowerCamelCase    :    Any 	=			{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
								self.assertEqual(zero_shot_classifier.entailment_id       ,-1)

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

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

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

								__lowerCamelCase    :    Union[str, Any] 	=			original_labelaid
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,zero_shot_classifier.entailment_id)
							@require_torch
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    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_0_0       ,candidate_labels=['politics', 'public health', 'science'])
							@require_torch
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			pipeline(
								    'zero-shot-classification'       ,model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,framework='pt'       ,)
								__lowerCamelCase    :    Optional[Any] 	=			zero_shot_classifier(
								    'Who are you voting for in 2020?'       ,candidate_labels=['politics', 'public health', 'science'])

								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        'sequence': 'Who are you voting for in 2020?',
								        'labels': ['science', 'public health', 'politics'],
								        'scores': [0.333, 0.333, 0.333],
								    }       ,)
							@require_tf
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Optional[Any] 	=			pipeline(
								    'zero-shot-classification'       ,model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,framework='tf'       ,)
								__lowerCamelCase    :    Optional[Any] 	=			zero_shot_classifier(
								    'Who are you voting for in 2020?'       ,candidate_labels=['politics', 'public health', 'science'])

								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        'sequence': 'Who are you voting for in 2020?',
								        'labels': ['science', 'public health', 'politics'],
								        'scores': [0.333, 0.333, 0.333],
								    }       ,)
							@slow
							@require_torch
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			pipeline('zero-shot-classification'       ,model='roberta-large-mnli'       ,framework='pt')
								__lowerCamelCase    :    Any 	=			zero_shot_classifier(
								    'Who are you voting for in 2020?'       ,candidate_labels=['politics', 'public health', 'science'])

								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        'sequence': 'Who are you voting for in 2020?',
								        'labels': ['politics', 'public health', 'science'],
								        'scores': [0.976, 0.015, 0.009],
								    }       ,)
								__lowerCamelCase    :    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=SCREAMING_SNAKE_CASE__       ,)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        '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    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			pipeline('zero-shot-classification'       ,model='roberta-large-mnli'       ,framework='tf')
								__lowerCamelCase    :    Dict 	=			zero_shot_classifier(
								    'Who are you voting for in 2020?'       ,candidate_labels=['politics', 'public health', 'science'])

								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        'sequence': 'Who are you voting for in 2020?',
								        'labels': ['politics', 'public health', 'science'],
								        'scores': [0.976, 0.015, 0.009],
								    }       ,)
								__lowerCamelCase    :    Union[str, Any] 	=			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=SCREAMING_SNAKE_CASE__       ,)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        '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],
								    }       ,)







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					None:
	__lowerCamelCase    :    int 	=			len(lowerCamelCase__      )

	# If row is equal to the size of the board it means there are a queen in each row in
	# the current board (possible_board)
	if row == n:
		# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
		# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
		boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board]      )
		return

	# We iterate each column in the row to find all possible results in each row
	for col in range(lowerCamelCase__      ):
		# We apply that we learned previously. First we check that in the current board
		# (possible_board) there are not other same value because if there is it means
		# that there are a collision in vertical. Then we apply the two formulas we
		# learned before:
		#
		# 45º: y - x = b or 45: row - col = b
		# 135º: y + x = b or row + col = b.
		#
		# And we verify if the results of this two formulas not exist in their variables
		# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
		#
		# If any or these are True it means there is a collision so we continue to the
		# next value in the for loop.
		if (
		    col in possible_board
		    or row - col in diagonal_right_collisions
		    or row + col in diagonal_left_collisions
		):
			continue

		# If it is False we call dfs function again and we update the inputs
		depth_first_search(
		    [*possible_board, col]     ,			[*diagonal_right_collisions, row - col]     ,			[*diagonal_left_collisions, row + col]     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    list[list[str]] 	=			[]
	depth_first_search([]     ,			[]     ,			[]     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	# Print all the boards
	for board in boards:
		for column in board:
			print(lowerCamelCase__      )
		print(''      )

	print(len(lowerCamelCase__      )     ,			'solutions were found.'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					n_queens_solution(4)




