
from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFRoFormerForCausalLM,
			    TFRoFormerForMaskedLM,
			    TFRoFormerForMultipleChoice,
			    TFRoFormerForQuestionAnswering,
			    TFRoFormerForSequenceClassification,
			    TFRoFormerForTokenClassification,
			    TFRoFormerModel,
			)
			from transformers.models.roformer.modeling_tf_roformer import (
			    TFRoFormerSelfAttention,
			    TFRoFormerSinusoidalPositionalEmbedding,
			)




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   List[str]      ,  lowercase     :   List[str]      ,  lowercase     :   Optional[Any]=13      ,  lowercase     :   Dict=7      ,  lowercase     :   Tuple=True      ,  lowercase     :   Tuple=True      ,  lowercase     :   Any=True      ,  lowercase     :   List[str]=True      ,  lowercase     :   Optional[int]=99      ,  lowercase     :   List[Any]=32      ,  lowercase     :   int=2      ,  lowercase     :   Dict=4      ,  lowercase     :   Optional[int]=37      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Dict=0.1      ,  lowercase     :   str=0.1      ,  lowercase     :   Optional[Any]=512      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Any=2      ,  lowercase     :   str=0.02      ,  lowercase     :   Any=3      ,  lowercase     :   List[str]=4      ,  lowercase     :   List[str]=None      ,  ):


				'''simple docstring'''


				_snake_case          =      parent
				_snake_case          =      13
				_snake_case          =      7
				_snake_case          =      True
				_snake_case          =      True
				_snake_case          =      True
				_snake_case          =      True
				_snake_case          =      99
				_snake_case          =      32
				_snake_case          =      2
				_snake_case          =      4
				_snake_case          =      37
				_snake_case          =      'gelu'
				_snake_case          =      0.1
				_snake_case          =      0.1
				_snake_case          =      512
				_snake_case          =      16
				_snake_case          =      2
				_snake_case          =      0.02
				_snake_case          =      3
				_snake_case          =      4
				_snake_case          =      None



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size     )

				_snake_case          =      None
				if self.use_input_mask:
							_snake_case          =      random_attention_mask([self.batch_size, self.seq_length]     )

				_snake_case          =      None
				if self.use_token_type_ids:
							_snake_case          =      ids_tensor([self.batch_size, self.seq_length]      ,  self.type_vocab_size     )

				_snake_case          =      None
				_snake_case          =      None
				_snake_case          =      None
				if self.use_labels:
							_snake_case          =      ids_tensor([self.batch_size]      ,  self.type_sequence_label_size     )
							_snake_case          =      ids_tensor([self.batch_size, self.seq_length]      ,  self.num_labels     )
							_snake_case          =      ids_tensor([self.batch_size]      ,  self.num_choices     )

				_snake_case          =      RoFormerConfig(
				    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      ,  return_dict=lowercase      ,  )

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



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Any      ,  lowercase     :   List[Any]      ,  lowercase     :   Tuple      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      TFRoFormerModel(config=lowercase     )
				_snake_case          =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

				_snake_case          =      [input_ids, input_mask]
				_snake_case          =      model(lowercase     )

				_snake_case          =      model(lowercase     )

				self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   str      ,  lowercase     :   List[Any]      ,  lowercase     :   Any      ,  lowercase     :   Dict      ,  lowercase     :   int      ,  lowercase     :   int      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      True
				_snake_case          =      TFRoFormerForCausalLM(config=lowercase     )
				_snake_case          =      {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}
				_snake_case          =      model(lowercase     )['logits']
				self.parent.assertListEqual(
				    list(prediction_scores.numpy().shape     )      ,  [self.batch_size, self.seq_length, self.vocab_size]     )



	def 			A							(  self     :   int      ,  lowercase     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   List[str]      ,  lowercase     :   List[Any]      ,  lowercase     :   Dict      ,  lowercase     :   int      ,  lowercase     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      TFRoFormerForMaskedLM(config=lowercase     )
				_snake_case          =      {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}
				_snake_case          =      model(lowercase     )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)     )



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   int      ,  lowercase     :   Optional[int]      ,  lowercase     :   List[str]      ,  lowercase     :   List[str]      ,  lowercase     :   Any      ,  lowercase     :   Dict      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.num_labels
				_snake_case          =      TFRoFormerForSequenceClassification(config=lowercase     )
				_snake_case          =      {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}

				_snake_case          =      model(lowercase     )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)     )



	def 			A							(  self     :   str      ,  lowercase     :   Any      ,  lowercase     :   Optional[int]      ,  lowercase     :   str      ,  lowercase     :   Optional[int]      ,  lowercase     :   Dict      ,  lowercase     :   Any      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.num_choices
				_snake_case          =      TFRoFormerForMultipleChoice(config=lowercase     )
				_snake_case          =      tf.tile(tf.expand_dims(lowercase      ,  1     )      ,  (1, self.num_choices, 1)     )
				_snake_case          =      tf.tile(tf.expand_dims(lowercase      ,  1     )      ,  (1, self.num_choices, 1)     )
				_snake_case          =      tf.tile(tf.expand_dims(lowercase      ,  1     )      ,  (1, self.num_choices, 1)     )
				_snake_case          =      {
				    'input_ids': multiple_choice_inputs_ids,
				    'attention_mask': multiple_choice_input_mask,
				    'token_type_ids': multiple_choice_token_type_ids,
				}
				_snake_case          =      model(lowercase     )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_choices)     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   Optional[int]      ,  lowercase     :   Tuple      ,  lowercase     :   str      ,  lowercase     :   Optional[Any]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.num_labels
				_snake_case          =      TFRoFormerForTokenClassification(config=lowercase     )
				_snake_case          =      {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}
				_snake_case          =      model(lowercase     )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.num_labels)     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Any      ,  lowercase     :   Optional[int]      ,  lowercase     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   int      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      TFRoFormerForQuestionAnswering(config=lowercase     )
				_snake_case          =      {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}

				_snake_case          =      model(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 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.prepare_config_and_inputs()
				(
				    (
				    _snake_case
				)     , (
				    _snake_case
				)     , (
				    _snake_case
				)     , (
				    _snake_case
				)     , (
				    _snake_case
				)     , (
				    _snake_case
				)     , (
				    _snake_case
				)     , 
				)          =      config_and_inputs
				_snake_case          =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
				return config, inputs_dict




@require_tf
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[int]          = (
	    (
	        TFRoFormerModel,
	        TFRoFormerForCausalLM,
	        TFRoFormerForMaskedLM,
	        TFRoFormerForQuestionAnswering,
	        TFRoFormerForSequenceClassification,
	        TFRoFormerForTokenClassification,
	        TFRoFormerForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	_UpperCAmelCase	:		Dict          = (
	    {
	        "feature-extraction": TFRoFormerModel,
	        "fill-mask": TFRoFormerForMaskedLM,
	        "question-answering": TFRoFormerForQuestionAnswering,
	        "text-classification": TFRoFormerForSequenceClassification,
	        "text-generation": TFRoFormerForCausalLM,
	        "token-classification": TFRoFormerForTokenClassification,
	        "zero-shot": TFRoFormerForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	_UpperCAmelCase	:		Dict          = False
	_UpperCAmelCase	:		Union[str, Any]          = False



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Tuple      ,  lowercase     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				if pipeline_test_casse_name == "TextGenerationPipelineTests":
							return True

				return False



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      TFRoFormerModelTester(self     )
				_snake_case          =      ConfigTester(self      ,  config_class=lowercase      ,  hidden_size=37     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				self.config_tester.run_common_tests()



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*lowercase     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head(*lowercase     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*lowercase     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*lowercase     )



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*lowercase     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*lowercase     )





	@slow
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'     )
				self.assertIsNotNone(lowercase     )




@require_tf
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



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


				'''simple docstring'''


				_snake_case          =      TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'     )
				_snake_case          =      tf.constant([[0, 1, 2, 3, 4, 5]]     )
				_snake_case          =      model(lowercase     )[0]

				# TODO Replace vocab size
				_snake_case          =      50_000

				_snake_case          =      [1, 6, vocab_size]
				self.assertEqual(output.shape      ,  lowercase     )

				print(output[:, :3, :3]     )

				# TODO Replace values below with what was printed above.
				_snake_case          =      tf.constant(
				    [
				        [
				            [-0.12053341, -1.0264901, 0.29221946],
				            [-1.5133783, 0.197433, 0.15190607],
				            [-5.0135403, -3.900256, -0.84038764],
				        ]
				    ]     )
				tf.debugging.assert_near(output[:, :3, :3]      ,  lowercase      ,  atol=1E-4     )




@require_tf
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = 1e-4



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      tf.constant([[4, 10]]     )
				_snake_case          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      ,  embedding_dim=6     )

				_snake_case          =      emba(input_ids.shape     )
				_snake_case          =      tf.constant(
				    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]     )

				tf.debugging.assert_near(lowercase      ,  lowercase      ,  atol=self.tolerance     )





	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      tf.constant(
				    [
				        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
				        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
				        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
				    ]     )
				_snake_case          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=512      ,  embedding_dim=512     )
				emba([2, 16, 512]     )
				_snake_case          =      emba.weight[:3, :5]

				tf.debugging.assert_near(lowercase      ,  lowercase      ,  atol=self.tolerance     )




@require_tf
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = 1e-4



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      tf.reshape(tf.range(2 * 12 * 16 * 64      ,  dtype=tf.floataa     )      ,  shape=(2, 12, 16, 64)     ) / 100

				_snake_case          =      -tf.reshape(tf.range(2 * 12 * 16 * 64      ,  dtype=tf.floataa     )      ,  shape=(2, 12, 16, 64)     ) / 100

				_snake_case          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32      ,  embedding_dim=64     )
				_snake_case          =      embed_positions([2, 16, 768]     )[None, None, :, :]

				_snake_case     , _snake_case          =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
				    lowercase      ,  lowercase      ,  lowercase     )

				_snake_case          =      tf.constant(
				    [
				        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
				        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
				        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
				        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
				        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
				        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
				    ]     )
				_snake_case          =      tf.constant(
				    [
				        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
				        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
				        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
				        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
				        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
				        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
				    ]     )

				tf.debugging.assert_near(query_layer[0, 0, :6, :8]      ,  lowercase      ,  atol=self.tolerance     )
				tf.debugging.assert_near(key_layer[0, 0, :6, :8]      ,  lowercase      ,  atol=self.tolerance     )

def 	a_		(	__lowercase       :     List[Any]			)			->  Tuple:
			_snake_case          =      len(__lowercase			)
			for i in range(length - 1			):
						_snake_case          =      i
						for k in range(i + 1	, __lowercase			):
									if collection[k] < collection[least]:
												_snake_case          =      k
						if least != i:
									_snake_case     , _snake_case          =      (collection[i], collection[least])
			return collection


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