





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 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Union[str, Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=7,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					List[str]=True,			_UpperCAmelCase     :					List[str]=True,			_UpperCAmelCase     :					Union[str, Any]=9_9,			_UpperCAmelCase     :					Any=3_2,			_UpperCAmelCase     :					str=2,			_UpperCAmelCase     :					Any=4,			_UpperCAmelCase     :					Union[str, Any]=3_7,			_UpperCAmelCase     :					List[str]="gelu",			_UpperCAmelCase     :					str=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					Any=5_1_2,			_UpperCAmelCase     :					Optional[Any]=1_6,			_UpperCAmelCase     :					str=2,			_UpperCAmelCase     :					Tuple=0.02,			_UpperCAmelCase     :					Optional[int]=3,			_UpperCAmelCase     :					Union[str, Any]=4,			_UpperCAmelCase     :					str=None,			)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						parent
														SCREAMING_SNAKE_CASE__       :      str     =						1_3
														SCREAMING_SNAKE_CASE__       :      Any     =						7
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						True
														SCREAMING_SNAKE_CASE__       :      int     =						True
														SCREAMING_SNAKE_CASE__       :      int     =						True
														SCREAMING_SNAKE_CASE__       :      int     =						9_9
														SCREAMING_SNAKE_CASE__       :      Dict     =						3_2
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						2
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						4
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						3_7
														SCREAMING_SNAKE_CASE__       :      Tuple     =						"gelu"
														SCREAMING_SNAKE_CASE__       :      List[str]     =						0.1
														SCREAMING_SNAKE_CASE__       :      str     =						0.1
														SCREAMING_SNAKE_CASE__       :      List[str]     =						5_1_2
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						1_6
														SCREAMING_SNAKE_CASE__       :      Any     =						2
														SCREAMING_SNAKE_CASE__       :      int     =						0.02
														SCREAMING_SNAKE_CASE__       :      str     =						3
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						4
														SCREAMING_SNAKE_CASE__       :      Tuple     =						None






							def  A_     ( self     :					int						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ids_tensor([self.batch_size, self.seq_length],			self.vocab_size						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						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__       :      Tuple     =						ids_tensor([self.batch_size, self.seq_length],			self.type_vocab_size						)

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

														SCREAMING_SNAKE_CASE__       :      List[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=_UpperCAmelCase,			)

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






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[Any]						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						TFRoFormerModel(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[input_ids, input_mask]
														SCREAMING_SNAKE_CASE__       :      Any     =						model(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Any     =						model(_UpperCAmelCase						)

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






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					int						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						True
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFRoFormerForCausalLM(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						{
														    "input_ids": input_ids,
														    "attention_mask": input_mask,
														    "token_type_ids": token_type_ids,
														}
														SCREAMING_SNAKE_CASE__       :      str     =						model(_UpperCAmelCase						)["logits"]
														self.parent.assertListEqual(
														    list(prediction_scores.numpy().shape						),			[self.batch_size, self.seq_length, self.vocab_size]						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Any						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						TFRoFormerForMaskedLM(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{
														    "input_ids": input_ids,
														    "attention_mask": input_mask,
														    "token_type_ids": token_type_ids,
														}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)						)






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					str						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.num_labels
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFRoFormerForSequenceClassification(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{
														    "input_ids": input_ids,
														    "attention_mask": input_mask,
														    "token_type_ids": token_type_ids,
														}

														SCREAMING_SNAKE_CASE__       :      Any     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.num_choices
														SCREAMING_SNAKE_CASE__       :      str     =						TFRoFormerForMultipleChoice(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						tf.tile(tf.expand_dims(_UpperCAmelCase,			1						),			(1, self.num_choices, 1)						)
														SCREAMING_SNAKE_CASE__       :      str     =						tf.tile(tf.expand_dims(_UpperCAmelCase,			1						),			(1, self.num_choices, 1)						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tf.tile(tf.expand_dims(_UpperCAmelCase,			1						),			(1, self.num_choices, 1)						)
														SCREAMING_SNAKE_CASE__       :      Any     =						{
														    "input_ids": multiple_choice_inputs_ids,
														    "attention_mask": multiple_choice_input_mask,
														    "token_type_ids": multiple_choice_token_type_ids,
														}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_choices)						)






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[str]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.num_labels
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						TFRoFormerForTokenClassification(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						{
														    "input_ids": input_ids,
														    "attention_mask": input_mask,
														    "token_type_ids": token_type_ids,
														}
														SCREAMING_SNAKE_CASE__       :      str     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.num_labels)						)






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFRoFormerForQuestionAnswering(config=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						{
														    "input_ids": input_ids,
														    "attention_mask": input_mask,
														    "token_type_ids": token_type_ids,
														}

														SCREAMING_SNAKE_CASE__       :      int     =						model(_UpperCAmelCase						)
														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     :					Dict						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.prepare_config_and_inputs()
														(
														    (
														    SCREAMING_SNAKE_CASE__
														)   ,(
														    SCREAMING_SNAKE_CASE__
														)   ,(
														    SCREAMING_SNAKE_CASE__
														)   ,(
														    SCREAMING_SNAKE_CASE__
														)   ,(
														    SCREAMING_SNAKE_CASE__
														)   ,(
														    SCREAMING_SNAKE_CASE__
														)   ,(
														    SCREAMING_SNAKE_CASE__
														)   ,
														)       :      Optional[int]     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      int     =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
														return config, inputs_dict


@require_tf
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   (
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCAmelCase_      						=   (
							    {
							        "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_      						=   False
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Tuple						)				->			Any:





														"""simple docstring"""
														if pipeline_test_casse_name == "TextGenerationPipelineTests":
																					return True

														return False






							def  A_     ( self     :					Optional[Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						TFRoFormerModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ConfigTester(self,			config_class=_UpperCAmelCase,			hidden_size=3_7						)






							def  A_     ( self     :					Optional[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)






							def  A_     ( self     :					List[str]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_lm_head(*_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase						)






							def  A_     ( self     :					Any						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase						)






							def  A_     ( self     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"						)
														self.assertIsNotNone(_UpperCAmelCase						)


@require_tf
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							@slow
							def  A_     ( self     :					List[Any]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"						)
														SCREAMING_SNAKE_CASE__       :      str     =						tf.constant([[0, 1, 2, 3, 4, 5]]						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						model(_UpperCAmelCase						)[0]

														# TODO Replace vocab size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						5_0_0_0_0

														SCREAMING_SNAKE_CASE__       :      Dict     =						[1, 6, vocab_size]
														self.assertEqual(output.shape,			_UpperCAmelCase						)

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

														# TODO Replace values below with what was printed above.
														SCREAMING_SNAKE_CASE__       :      List[str]     =						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],			_UpperCAmelCase,			atol=1E-4						)


@require_tf
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   1E-4






							def  A_     ( self     :					Optional[Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tf.constant([[4, 1_0]]						)
														SCREAMING_SNAKE_CASE__       :      Any     =						TFRoFormerSinusoidalPositionalEmbedding(num_positions=6,			embedding_dim=6						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						emba(input_ids.shape						)
														SCREAMING_SNAKE_CASE__       :      str     =						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(_UpperCAmelCase,			_UpperCAmelCase,			atol=self.tolerance						)






							def  A_     ( self     :					int						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						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],
														    ]						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2,			embedding_dim=5_1_2						)
														emba([2, 1_6, 5_1_2]						)
														SCREAMING_SNAKE_CASE__       :      str     =						emba.weight[:3, :5]

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


@require_tf
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   1E-4






							def  A_     ( self     :					List[Any]						)				->			Tuple:





														"""simple docstring"""
														# 2,12,16,64
														SCREAMING_SNAKE_CASE__       :      int     =						tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4,			dtype=tf.floataa						),			shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4,			dtype=tf.floataa						),			shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

														SCREAMING_SNAKE_CASE__       :      List[str]     =						TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2,			embedding_dim=6_4						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						embed_positions([2, 1_6, 7_6_8]						)[None, None, :, :]

														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						TFRoFormerSelfAttention.apply_rotary_position_embeddings(
														    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

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













def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    int:

							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
														raise ValueError("String lengths must match!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						0

							for chara, chara in zip(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														if chara != chara:
																					count += 1

							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()






