import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    ErnieMForInformationExtraction,
					    ErnieMForMultipleChoice,
					    ErnieMForQuestionAnswering,
					    ErnieMForSequenceClassification,
					    ErnieMForTokenClassification,
					    ErnieMModel,
					)
					from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST


class _a       :

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=13							,			__UpperCAmelCase=7							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase=99							,			__UpperCAmelCase=32							,			__UpperCAmelCase=5							,			__UpperCAmelCase=4							,			__UpperCAmelCase=37							,			__UpperCAmelCase="gelu"							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=512							,			__UpperCAmelCase=16							,			__UpperCAmelCase=2							,			__UpperCAmelCase=0.02							,			__UpperCAmelCase=3							,			__UpperCAmelCase=4							,			__UpperCAmelCase=None							,			):
			__A							:   str	  =				parent
			__A							:   Dict	  =				batch_size
			__A							:   Optional[int]	  =				seq_length
			__A							:   str	  =				is_training
			__A							:   int	  =				use_input_mask
			__A							:   Optional[Any]	  =				use_labels
			__A							:   int	  =				vocab_size
			__A							:   Any	  =				hidden_size
			__A							:   int	  =				num_hidden_layers
			__A							:   str	  =				num_attention_heads
			__A							:   Optional[int]	  =				intermediate_size
			__A							:   Union[str, Any]	  =				hidden_act
			__A							:   Tuple	  =				hidden_dropout_prob
			__A							:   int	  =				attention_probs_dropout_prob
			__A							:   Tuple	  =				max_position_embeddings
			__A							:   str	  =				type_vocab_size
			__A							:   Any	  =				type_sequence_label_size
			__A							:   int	  =				initializer_range
			__A							:   Tuple	  =				num_labels
			__A							:   int	  =				num_choices
			__A							:   Any	  =				scope

	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size   )

			__A							:   Dict	  =				None
			if self.use_input_mask:
					__A							:   List[Any]	  =				random_attention_mask([self.batch_size, self.seq_length]   )

			__A							:   Any	  =				None
			__A							:   Union[str, Any]	  =				None
			__A							:   int	  =				None
			if self.use_labels:
					__A							:   Optional[Any]	  =				ids_tensor([self.batch_size]							,			self.type_sequence_label_size   )
					__A							:   Any	  =				ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels   )
					__A							:   Union[str, Any]	  =				ids_tensor([self.batch_size]							,			self.num_choices   )

			__A							:   Optional[int]	  =				self.get_config()

			return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[int]	  =				ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size   )

			__A							:   Optional[Any]	  =				None
			if self.use_input_mask:
					__A							:   Optional[int]	  =				random_attention_mask([self.batch_size, self.seq_length]   )
			__A							:   List[Any]	  =				self.get_config()

			return config, input_ids, input_mask

	def 					__UpperCAmelCase(       self   ):
			return ErnieMConfig(
			    vocab_size=self.vocab_size							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			type_vocab_size=self.type_vocab_size							,			initializer_range=self.initializer_range							,			)

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Tuple	  =				ErnieMModel(config=lowercase__   )
			model.to(lowercase__   )
			model.eval()
			__A							:   Optional[int]	  =				model(lowercase__							,			return_dict=lowercase__   )
			self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				ErnieMForQuestionAnswering(config=lowercase__   )
			model.to(lowercase__   )
			model.eval()
			__A							:   List[Any]	  =				model(
			    lowercase__							,			attention_mask=lowercase__							,			start_positions=lowercase__							,			end_positions=lowercase__							,			)
			self.parent.assertEqual(result.start_logits.shape							,			(self.batch_size, self.seq_length)   )
			self.parent.assertEqual(result.end_logits.shape							,			(self.batch_size, self.seq_length)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   str	  =				ErnieMForInformationExtraction(config=lowercase__   )
			model.to(lowercase__   )
			model.eval()
			__A							:   List[str]	  =				torch.ones_like(lowercase__							,			dtype=torch.floataa   )
			__A							:   Tuple	  =				model(
			    lowercase__							,			attention_mask=lowercase__							,			start_positions=lowercase__							,			end_positions=lowercase__							,			)
			self.parent.assertEqual(result.start_logits.shape							,			(self.batch_size, self.seq_length)   )
			self.parent.assertEqual(result.end_logits.shape							,			(self.batch_size, self.seq_length)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Dict	  =				self.num_labels
			__A							:   List[Any]	  =				ErnieMForSequenceClassification(lowercase__   )
			model.to(lowercase__   )
			model.eval()
			__A							:   Optional[int]	  =				model(lowercase__							,			attention_mask=lowercase__							,			labels=lowercase__   )
			self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Dict	  =				self.num_labels
			__A							:   int	  =				ErnieMForTokenClassification(config=lowercase__   )
			model.to(lowercase__   )
			model.eval()
			input_ids.to(lowercase__   )
			input_mask.to(lowercase__   )
			token_labels.to(lowercase__   )

			__A							:   int	  =				model(lowercase__							,			attention_mask=lowercase__							,			labels=lowercase__   )

			self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				self.num_choices
			__A							:   Dict	  =				ErnieMForMultipleChoice(config=lowercase__   )
			model.to(lowercase__   )
			model.eval()
			__A							:   Any	  =				input_ids.unsqueeze(1   ).expand(-1							,			self.num_choices							,			-1   ).contiguous()
			__A							:   Dict	  =				input_mask.unsqueeze(1   ).expand(-1							,			self.num_choices							,			-1   ).contiguous()
			__A							:   Dict	  =				model(
			    lowercase__							,			attention_mask=lowercase__							,			labels=lowercase__							,			)
			self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_choices)   )







	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				self.prepare_config_and_inputs()
			(
			    __A						
			)							:   Optional[Any]	  =				config_and_inputs
			__A							:   Any	  =				{"input_ids": input_ids, "attention_mask": input_mask}
			return config, inputs_dict


@require_torch
class _a       (	_UpperCAmelCase   ,							_UpperCAmelCase   ,							unittest.TestCase ):

	'''simple docstring'''



	lowerCamelCase_   :      List[str]       =	(
	    (
	        ErnieMModel,
	        ErnieMForMultipleChoice,
	        ErnieMForQuestionAnswering,
	        ErnieMForSequenceClassification,
	        ErnieMForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase_   :      Union[str, Any]       =	()
	lowerCamelCase_   :      Union[str, Any]       =	(
	    {
	        """feature-extraction""": ErnieMModel,
	        """question-answering""": ErnieMForQuestionAnswering,
	        """text-classification""": ErnieMForSequenceClassification,
	        """token-classification""": ErnieMForTokenClassification,
	        """zero-shot""": ErnieMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCamelCase_   :      Tuple       =	False

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):






			if pipeline_test_casse_name == "QAPipelineTests":
					return True

			return False

	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				ErnieMModelTester(self   )
			__A							:   List[Any]	  =				ConfigTester(self							,			config_class=lowercase__							,			hidden_size=37   )

	def 					__UpperCAmelCase(       self   ):
			self.config_tester.run_common_tests()

	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowercase__   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[Any]	  =				self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					__A							:   Dict	  =				type
					self.model_tester.create_and_check_model(*lowercase__   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[int]	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*lowercase__   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowercase__   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowercase__   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[Any]	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_information_extraction(*lowercase__   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowercase__   )







	@slow
	def 					__UpperCAmelCase(       self   ):






			for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__A							:   str	  =				ErnieMModel.from_pretrained(lowercase__   )
					self.assertIsNotNone(lowercase__   )


@require_torch
class _a       (	unittest.TestCase ):

	'''simple docstring'''



	@slow
	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				ErnieMModel.from_pretrained("susnato/ernie-m-base_pytorch"   )
			model.eval()
			__A							:   List[Any]	  =				torch.tensor([[0, 1, 2, 3, 4, 5]]   )
			__A							:   Optional[Any]	  =				model(lowercase__   )[0]

			# TODO Replace vocab size
			__A							:   Tuple	  =				768

			__A							:   Optional[int]	  =				torch.Size((1, 6, hidden_size)   )
			self.assertEqual(output.shape							,			lowercase__   )

			__A							:   List[str]	  =				torch.tensor(
			    [[[-0.00_12, 0.12_45, -0.02_14], [-0.07_42, 0.02_44, -0.07_71], [-0.03_33, 0.11_64, -0.15_54]]]   )

			self.assertTrue(torch.allclose(output[:, :3, :3]							,			lowercase__							,			atol=1e-3   )   )






def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Tuple:
		__A							:   Union[str, Any]	  =				index
		__A							:   int	  =				2 * index + 1
		__A							:   Dict	  =				2 * index + 2
		if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
				__A							:   Dict	  =				left_index

		if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
				__A							:   int	  =				right_index

		if largest != index:
				__A  ,       __A							:   Dict	  =				unsorted[index], unsorted[largest]
				heapify(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	Tuple:
		__A							:   Dict	  =				len(_lowercase    )
		for i in range(n // 2 - 1 ,     -1 ,     -1    ):
				heapify(_lowercase ,     _lowercase ,     _lowercase    )
		for i in range(n - 1 ,     0 ,     -1    ):
				__A  ,       __A							:   Dict	  =				unsorted[i], unsorted[0]
				heapify(_lowercase ,     0 ,     _lowercase    )
		return unsorted


if __name__ == "__main__":
					UpperCamelCase      			=     input('Enter numbers separated by a comma:\n').strip()
					UpperCamelCase      			=     [int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))





