

'''simple docstring'''

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 							_snake_case :
			def __init__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=13     ,SCREAMING_SNAKE_CASE__			:      List[str]=7     ,SCREAMING_SNAKE_CASE__			:      int=True     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=True     ,SCREAMING_SNAKE_CASE__			:      str=True     ,SCREAMING_SNAKE_CASE__			:      Dict=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=99     ,SCREAMING_SNAKE_CASE__			:      int=32     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=2     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=4     ,SCREAMING_SNAKE_CASE__			:      List[str]=37     ,SCREAMING_SNAKE_CASE__			:      List[Any]="gelu"     ,SCREAMING_SNAKE_CASE__			:      List[str]=0.1     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=0.1     ,SCREAMING_SNAKE_CASE__			:      int=512     ,SCREAMING_SNAKE_CASE__			:      Tuple=16     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=2     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=0.02     ,SCREAMING_SNAKE_CASE__			:      List[Any]=3     ,SCREAMING_SNAKE_CASE__			:      List[str]=4     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,):
					SCREAMING_SNAKE_CASE:List[str]						      =					parent
					SCREAMING_SNAKE_CASE:Optional[int]						      =					13
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					7
					SCREAMING_SNAKE_CASE:List[Any]						      =					True
					SCREAMING_SNAKE_CASE:List[Any]						      =					True
					SCREAMING_SNAKE_CASE:List[Any]						      =					True
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					True
					SCREAMING_SNAKE_CASE:List[str]						      =					99
					SCREAMING_SNAKE_CASE:Dict						      =					32
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					2
					SCREAMING_SNAKE_CASE:Dict						      =					4
					SCREAMING_SNAKE_CASE:int						      =					37
					SCREAMING_SNAKE_CASE:List[Any]						      =					"gelu"
					SCREAMING_SNAKE_CASE:List[str]						      =					0.1
					SCREAMING_SNAKE_CASE:List[str]						      =					0.1
					SCREAMING_SNAKE_CASE:str						      =					512
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					16
					SCREAMING_SNAKE_CASE:str						      =					2
					SCREAMING_SNAKE_CASE:Dict						      =					0.02
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					3
					SCREAMING_SNAKE_CASE:Optional[int]						      =					4
					SCREAMING_SNAKE_CASE:int						      =					None
			def     __UpperCamelCase						( self			:      List[Any]							):
					SCREAMING_SNAKE_CASE:str						      =					ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size							)

					SCREAMING_SNAKE_CASE:List[str]						      =					None
					if self.use_input_mask:
							SCREAMING_SNAKE_CASE:Tuple						      =					random_attention_mask([self.batch_size, self.seq_length]							)

					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					None
					if self.use_token_type_ids:
							SCREAMING_SNAKE_CASE:Optional[int]						      =					ids_tensor([self.batch_size, self.seq_length]     ,self.type_vocab_size							)

					SCREAMING_SNAKE_CASE:int						      =					None
					SCREAMING_SNAKE_CASE:str						      =					None
					SCREAMING_SNAKE_CASE:Dict						      =					None
					if self.use_labels:
							SCREAMING_SNAKE_CASE:Dict						      =					ids_tensor([self.batch_size]     ,self.type_sequence_label_size							)
							SCREAMING_SNAKE_CASE:Tuple						      =					ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels							)
							SCREAMING_SNAKE_CASE:int						      =					ids_tensor([self.batch_size]     ,self.num_choices							)

					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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=SCREAMING_SNAKE_CASE__     ,)

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
					SCREAMING_SNAKE_CASE:List[str]						      =					TFRoFormerModel(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:int						      =					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[input_ids, input_mask]
					SCREAMING_SNAKE_CASE:str						      =					model(SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:Tuple						      =					model(SCREAMING_SNAKE_CASE__							)

					self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)							)
			def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
					SCREAMING_SNAKE_CASE:Any						      =					True
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					TFRoFormerForCausalLM(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model(SCREAMING_SNAKE_CASE__							)["logits"]
					self.parent.assertListEqual(
					    list(prediction_scores.numpy().shape							)     ,[self.batch_size, self.seq_length, self.vocab_size]							)
			def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
					SCREAMING_SNAKE_CASE:int						      =					TFRoFormerForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Dict						      =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					SCREAMING_SNAKE_CASE:Optional[int]						      =					model(SCREAMING_SNAKE_CASE__							)
					self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)							)
			def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str							):
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.num_labels
					SCREAMING_SNAKE_CASE:int						      =					TFRoFormerForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}

					SCREAMING_SNAKE_CASE:int						      =					model(SCREAMING_SNAKE_CASE__							)
					self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)							)
			def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
					SCREAMING_SNAKE_CASE:Tuple						      =					self.num_choices
					SCREAMING_SNAKE_CASE:Any						      =					TFRoFormerForMultipleChoice(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Optional[int]						      =					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__     ,1							)     ,(1, self.num_choices, 1)							)
					SCREAMING_SNAKE_CASE:Optional[int]						      =					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__     ,1							)     ,(1, self.num_choices, 1)							)
					SCREAMING_SNAKE_CASE:Dict						      =					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__     ,1							)     ,(1, self.num_choices, 1)							)
					SCREAMING_SNAKE_CASE:str						      =					{
					    "input_ids": multiple_choice_inputs_ids,
					    "attention_mask": multiple_choice_input_mask,
					    "token_type_ids": multiple_choice_token_type_ids,
					}
					SCREAMING_SNAKE_CASE:int						      =					model(SCREAMING_SNAKE_CASE__							)
					self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_choices)							)
			def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
					SCREAMING_SNAKE_CASE:List[Any]						      =					self.num_labels
					SCREAMING_SNAKE_CASE:Dict						      =					TFRoFormerForTokenClassification(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Tuple						      =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					SCREAMING_SNAKE_CASE:List[Any]						      =					model(SCREAMING_SNAKE_CASE__							)
					self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels)							)
			def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
					SCREAMING_SNAKE_CASE:str						      =					TFRoFormerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Any						      =					{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}

					SCREAMING_SNAKE_CASE:Any						      =					model(SCREAMING_SNAKE_CASE__							)
					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			:      int							):
					SCREAMING_SNAKE_CASE:List[Any]						      =					self.prepare_config_and_inputs()
					(
					    SCREAMING_SNAKE_CASE    
					):List[str]						      =					config_and_inputs
					SCREAMING_SNAKE_CASE:Optional[int]						      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict





@require_tf
class 							_snake_case (       _a	,  _a	,  unittest.TestCase						):
			_A       :     str       =					(
			    (
			        TFRoFormerModel,
			        TFRoFormerForCausalLM,
			        TFRoFormerForMaskedLM,
			        TFRoFormerForQuestionAnswering,
			        TFRoFormerForSequenceClassification,
			        TFRoFormerForTokenClassification,
			        TFRoFormerForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			_A       :     Union[str, Any]       =					(
			    {
			        '''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 {}
			)

			_A       :     List[Any]       =					False
			_A       :     Any       =					False
			def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
					if pipeline_test_casse_name == "TextGenerationPipelineTests":
							return True

					return False
			def     __UpperCamelCase						( self			:      List[Any]							):
					SCREAMING_SNAKE_CASE:Optional[int]						      =					TFRoFormerModelTester(self							)
					SCREAMING_SNAKE_CASE:Any						      =					ConfigTester(self     ,config_class=SCREAMING_SNAKE_CASE__     ,hidden_size=37							)
			def     __UpperCamelCase						( self			:      Tuple							):
					self.config_tester.run_common_tests()
			def     __UpperCamelCase						( self			:      Dict							):
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      List[Any]							):
					SCREAMING_SNAKE_CASE:Any						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      Tuple							):
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_lm_head(*SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      int							):
					SCREAMING_SNAKE_CASE:Any						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      Optional[int]							):
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      Any							):
					SCREAMING_SNAKE_CASE:Tuple						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__							)
			def     __UpperCamelCase						( self			:      List[Any]							):
					SCREAMING_SNAKE_CASE:int						      =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__							)







			@slow
			def     __UpperCamelCase						( self			:      Optional[Any]							):
					SCREAMING_SNAKE_CASE:List[str]						      =					TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"							)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)





@require_tf
class 							_snake_case (       unittest.TestCase						):
			@slow
			def     __UpperCamelCase						( self			:      List[str]							):
					SCREAMING_SNAKE_CASE:Dict						      =					TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"							)
					SCREAMING_SNAKE_CASE:Any						      =					tf.constant([[0, 1, 2, 3, 4, 5]]							)
					SCREAMING_SNAKE_CASE:Dict						      =					model(SCREAMING_SNAKE_CASE__							)[0]

					# TODO Replace vocab size
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					50_000

					SCREAMING_SNAKE_CASE:List[Any]						      =					[1, 6, vocab_size]
					self.assertEqual(output.shape     ,SCREAMING_SNAKE_CASE__							)

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

					# TODO Replace values below with what was printed above.
					SCREAMING_SNAKE_CASE:List[str]						      =					tf.constant(
					    [
					        [
					            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
					            [-1.5_133_783, 0.197_433, 0.15_190_607],
					            [-5.0_135_403, -3.900_256, -0.84_038_764],
					        ]
					    ]							)
					tf.debugging.assert_near(output[:, :3, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-4							)





@require_tf
class 							_snake_case (       unittest.TestCase						):
			_A       :     int       =					1E-4
			def     __UpperCamelCase						( self			:      Optional[int]							):
					SCREAMING_SNAKE_CASE:Any						      =					tf.constant([[4, 10]]							)
					SCREAMING_SNAKE_CASE:Dict						      =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,embedding_dim=6							)

					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					emba(input_ids.shape							)
					SCREAMING_SNAKE_CASE:List[str]						      =					tf.constant(
					    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]							)

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







			def     __UpperCamelCase						( self			:      int							):
					SCREAMING_SNAKE_CASE:List[Any]						      =					tf.constant(
					    [
					        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
					        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
					        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
					    ]							)
					SCREAMING_SNAKE_CASE:List[Any]						      =					TFRoFormerSinusoidalPositionalEmbedding(num_positions=512     ,embedding_dim=512							)
					emba([2, 16, 512]							)
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					emba.weight[:3, :5]

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





@require_tf
class 							_snake_case (       unittest.TestCase						):
			_A       :     Any       =					1E-4
			def     __UpperCamelCase						( self			:      Dict							):
					# 2,12,16,64
					SCREAMING_SNAKE_CASE:Dict						      =					tf.reshape(tf.range(2 * 12 * 16 * 64     ,dtype=tf.floataa							)     ,shape=(2, 12, 16, 64)							) / 100

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

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

					SCREAMING_SNAKE_CASE:str						      =					TFRoFormerSelfAttention.apply_rotary_position_embeddings(
					    SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:Tuple						      =					tf.constant(
					    [
					        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
					        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
					        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
					        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
					        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
					        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
					    ]							)
					SCREAMING_SNAKE_CASE:Optional[int]						      =					tf.constant(
					    [
					        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
					        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
					        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
					        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
					        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
					        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
					    ]							)

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









'''simple docstring'''

import random





def    A_  ( snake_case      ,       snake_case      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:dict						      =					{i: [] for i in range(snake_case )}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(snake_case )
  # 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(snake_case ):
    for j in range(i + 1      ,       snake_case ):
      if random.random() < probability:
        graph[i].append(snake_case )
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(snake_case )
  return graph





def    A_  ( snake_case ):
  return {
      i: [j for j in range(snake_case ) if i != j] for i in range(snake_case )
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()






