
from __future__ import annotations

import math




def 							a		(				snake_case__:   list		,  snake_case__:   list   ):


				'''simple docstring'''
				if len(snake_case__   ) != 2 or len(a[0]   ) != 2 or len(snake_case__   ) != 2 or len(b[0]   ) != 2:
								raise Exception('''Matrices are not 2x2'''   )
				lowercase_	    =   [
				    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
				    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
				]
				return new_matrix




def 							a		(				snake_case__:   list		,  snake_case__:   list   ):


				'''simple docstring'''
				return [
				    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]   )   )]
				    for row in range(len(snake_case__   )   )
				]




def 							a		(				snake_case__:   list		,  snake_case__:   list   ):


				'''simple docstring'''
				return [
				    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]   )   )]
				    for row in range(len(snake_case__   )   )
				]




def 							a		(				snake_case__:   list   ):


				'''simple docstring'''
				if len(snake_case__   ) % 2 != 0 or len(a[0]   ) % 2 != 0:
								raise Exception('''Odd matrices are not supported!'''   )

				lowercase_	    =   len(snake_case__   )
				lowercase_	    =   matrix_length // 2

				lowercase_	    =   [[a[i][j] for j in range(snake_case__		,  snake_case__   )] for i in range(snake_case__   )]
				lowercase_	    =   [
				    [a[i][j] for j in range(snake_case__		,  snake_case__   )] for i in range(snake_case__		,  snake_case__   )
				]

				lowercase_	    =   [[a[i][j] for j in range(snake_case__   )] for i in range(snake_case__   )]
				lowercase_	    =   [[a[i][j] for j in range(snake_case__   )] for i in range(snake_case__		,  snake_case__   )]

				return top_left, top_right, bot_left, bot_right




def 							a		(				snake_case__:   list   ):


				'''simple docstring'''
				return len(snake_case__   ), len(matrix[0]   )




def 							a		(				snake_case__:   list   ):


				'''simple docstring'''
				print('''\n'''.join(str(snake_case__   ) for line in matrix   )   )




def 							a		(				snake_case__:   list		,  snake_case__:   list   ):


				'''simple docstring'''
				if matrix_dimensions(snake_case__   ) == (2, 2):
								return default_matrix_multiplication(snake_case__		,  snake_case__   )

				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   split_matrix(snake_case__   )
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   split_matrix(snake_case__   )

				lowercase_	    =   actual_strassen(snake_case__		,  matrix_subtraction(snake_case__		,  snake_case__   )   )
				lowercase_	    =   actual_strassen(matrix_addition(snake_case__		,  snake_case__   )		,  snake_case__   )
				lowercase_	    =   actual_strassen(matrix_addition(snake_case__		,  snake_case__   )		,  snake_case__   )
				lowercase_	    =   actual_strassen(snake_case__		,  matrix_subtraction(snake_case__		,  snake_case__   )   )
				lowercase_	    =   actual_strassen(matrix_addition(snake_case__		,  snake_case__   )		,  matrix_addition(snake_case__		,  snake_case__   )   )
				lowercase_	    =   actual_strassen(matrix_subtraction(snake_case__		,  snake_case__   )		,  matrix_addition(snake_case__		,  snake_case__   )   )
				lowercase_	    =   actual_strassen(matrix_subtraction(snake_case__		,  snake_case__   )		,  matrix_addition(snake_case__		,  snake_case__   )   )

				lowercase_	    =   matrix_addition(matrix_subtraction(matrix_addition(snake_case__		,  snake_case__   )		,  snake_case__   )		,  snake_case__   )
				lowercase_	    =   matrix_addition(snake_case__		,  snake_case__   )
				lowercase_	    =   matrix_addition(snake_case__		,  snake_case__   )
				lowercase_	    =   matrix_subtraction(matrix_subtraction(matrix_addition(snake_case__		,  snake_case__   )		,  snake_case__   )		,  snake_case__   )

				# construct the new matrix from our 4 quadrants
				lowercase_	    =   []
				for i in range(len(snake_case__   )   ):
								new_matrix.append(top_left[i] + top_right[i]   )
				for i in range(len(snake_case__   )   ):
								new_matrix.append(bot_left[i] + bot_right[i]   )
				return new_matrix




def 							a		(				snake_case__:   list		,  snake_case__:   list   ):


				'''simple docstring'''
				if matrix_dimensions(snake_case__   )[1] != matrix_dimensions(snake_case__   )[0]:
								lowercase_	    =   (
								    '''Unable to multiply these matrices, please check the dimensions.\n'''
								    F'''Matrix A: {matrixa}\n'''
								    F'''Matrix B: {matrixa}'''
								)
								raise Exception(snake_case__   )
				lowercase_	    =   matrix_dimensions(snake_case__   )
				lowercase_	    =   matrix_dimensions(snake_case__   )

				if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
								return [matrixa, matrixa]

				lowercase_	    =   max(*snake_case__		,  *snake_case__   )
				lowercase_	    =   int(math.pow(2		,  math.ceil(math.loga(snake_case__   )   )   )   )
				lowercase_	    =   matrixa
				lowercase_	    =   matrixa

				# Adding zeros to the matrices so that the arrays dimensions are the same and also
				# power of 2
				for i in range(0		,  snake_case__   ):
								if i < dimensiona[0]:
												for _ in range(dimensiona[1]		,  snake_case__   ):
																new_matrixa[i].append(0   )
								else:
												new_matrixa.append([0] * maxim   )
								if i < dimensiona[0]:
												for _ in range(dimensiona[1]		,  snake_case__   ):
																new_matrixa[i].append(0   )
								else:
												new_matrixa.append([0] * maxim   )

				lowercase_	    =   actual_strassen(snake_case__		,  snake_case__   )

				# Removing the additional zeros
				for i in range(0		,  snake_case__   ):
								if i < dimensiona[0]:
												for _ in range(dimensiona[1]		,  snake_case__   ):
																final_matrix[i].pop()
								else:
												final_matrix.pop()
				return final_matrix


if __name__ == "__main__":
						__a         =   [
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 2, 3, 1],
						]
						__a         =   [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
						print(strassen(matrixa, matrixa))






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						)
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str=1_3			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=7			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=9_9			,				SCREAMING_SNAKE_CASE_    :     List[str]=3_2			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=4			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=3_7			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="gelu"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0.02			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     List[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="None"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=3			,				SCREAMING_SNAKE_CASE_    :     List[Any]=4			,				SCREAMING_SNAKE_CASE_    :     str=None			,				)			->	str:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   is_training
					lowercase_	    =   use_input_mask
					lowercase_	    =   use_token_type_ids
					lowercase_	    =   use_labels
					lowercase_	    =   vocab_size
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   type_vocab_size
					lowercase_	    =   type_sequence_label_size
					lowercase_	    =   initializer_range
					lowercase_	    =   num_labels
					lowercase_	    =   num_choices
					lowercase_	    =   relative_attention
					lowercase_	    =   position_biased_input
					lowercase_	    =   pos_att_type
					lowercase_	    =   scope
	def 		_lowercase    (			self    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

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

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

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

					lowercase_	    =   DebertaVaConfig(
					    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			,				relative_attention=self.relative_attention			,				position_biased_input=self.position_biased_input			,				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 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   TFDebertaVaModel(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

					lowercase_	    =   [input_ids, input_mask]
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Union[str, Any]:
					lowercase_	    =   TFDebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   TFDebertaVaForSequenceClassification(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Optional[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   TFDebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	str:
					lowercase_	    =   TFDebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}

					lowercase_	    =   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 		_lowercase    (			self    :     List[str]			)			->	Optional[int]:
					lowercase_	    =   self.prepare_config_and_inputs()
					(
					    (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      
					)	    =   config_and_inputs
					lowercase_	    =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

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



	"""simple docstring"""




	a     :str       	=      (
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	a     :int       	=      (
	    {
	        'feature-extraction': TFDebertaVaModel,
	        'fill-mask': TFDebertaVaForMaskedLM,
	        'question-answering': TFDebertaVaForQuestionAnswering,
	        'text-classification': TFDebertaVaForSequenceClassification,
	        'token-classification': TFDebertaVaForTokenClassification,
	        'zero-shot': TFDebertaVaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	a     :int       	=      False
	a     :Any       	=      False
	def 		_lowercase    (			self    :     Dict			)			->	str:
					lowercase_	    =   TFDebertaVaModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	List[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[int]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Any			)			->	str:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Optional[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_			)




	@slow
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					lowercase_	    =   TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''			)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

@require_tf
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@unittest.skip(reason='''Model not available yet'''			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					pass




	@slow
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					lowercase_	    =   TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''			)
					lowercase_	    =   tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
					lowercase_	    =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			)[0]

					lowercase_	    =   tf.constant(
					    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]			)
					tf.debugging.assert_near(output[:, 1:4, 1:4]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)




