


'''simple docstring'''



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class 						_snake_case   :




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=99							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=9							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase=32							,							_lowerCamelCase=5							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase=8							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.002							,							_lowerCamelCase=1							,							_lowerCamelCase=0							,							_lowerCamelCase=0							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							):
								UpperCAmelCase__					:   str       				=       parent
								UpperCAmelCase__					:   str       				=       batch_size
								UpperCAmelCase__					:   List[str]       				=       encoder_seq_length
								UpperCAmelCase__					:   Tuple       				=       decoder_seq_length
								# For common tests
								UpperCAmelCase__					:   str       				=       self.decoder_seq_length
								UpperCAmelCase__					:   Optional[int]       				=       is_training
								UpperCAmelCase__					:   Optional[int]       				=       use_attention_mask
								UpperCAmelCase__					:   Optional[int]       				=       use_labels
								UpperCAmelCase__					:   int       				=       vocab_size
								UpperCAmelCase__					:   Dict       				=       hidden_size
								UpperCAmelCase__					:   Tuple       				=       num_hidden_layers
								UpperCAmelCase__					:   Dict       				=       num_attention_heads
								UpperCAmelCase__					:   Optional[int]       				=       d_ff
								UpperCAmelCase__					:   Optional[int]       				=       relative_attention_num_buckets
								UpperCAmelCase__					:   Dict       				=       dropout_rate
								UpperCAmelCase__					:   Union[str, Any]       				=       initializer_factor
								UpperCAmelCase__					:   str       				=       eos_token_id
								UpperCAmelCase__					:   int       				=       pad_token_id
								UpperCAmelCase__					:   Dict       				=       decoder_start_token_id
								UpperCAmelCase__					:   Union[str, Any]       				=       None
								UpperCAmelCase__					:   List[Any]       				=       decoder_layers




					def 			snake_case__  ( self):
								return TaConfig.from_pretrained("""google/umt5-base""")




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							):
								if attention_mask is None:
											UpperCAmelCase__					:   int       				=       input_ids.ne(config.pad_token_id)
								if decoder_attention_mask is None:
											UpperCAmelCase__					:   Dict       				=       decoder_input_ids.ne(config.pad_token_id)
								if head_mask is None:
											UpperCAmelCase__					:   Union[str, Any]       				=       torch.ones(config.num_hidden_layers							,							config.num_attention_heads							,							device=_lowerCamelCase)
								if decoder_head_mask is None:
											UpperCAmelCase__					:   str       				=       torch.ones(config.num_decoder_layers							,							config.num_attention_heads							,							device=_lowerCamelCase)
								if cross_attn_head_mask is None:
											UpperCAmelCase__					:   int       				=       torch.ones(
											    config.num_decoder_layers							,							config.num_attention_heads							,							device=_lowerCamelCase)
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       ids_tensor([self.batch_size, self.encoder_seq_length]							,							self.vocab_size)
								UpperCAmelCase__					:   int       				=       ids_tensor([self.batch_size, self.decoder_seq_length]							,							self.vocab_size)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								UpperCAmelCase__					:   Optional[Any]       				=       input_ids.clamp(self.pad_token_id + 1)
								UpperCAmelCase__					:   Union[str, Any]       				=       decoder_input_ids.clamp(self.pad_token_id + 1)

								UpperCAmelCase__					:   Any       				=       self.get_config()
								UpperCAmelCase__					:   List[Any]       				=       config.num_attention_heads
								UpperCAmelCase__					:   Dict       				=       self.prepare_inputs_dict(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
								return config, input_dict




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       self.prepare_config_and_inputs()
								return config, inputs_dict




					def 			snake_case__  ( self):
								return TaConfig(
								    vocab_size=166							,							d_model=self.hidden_size							,							d_ff=self.d_ff							,							d_kv=self.hidden_size // self.num_attention_heads							,							num_layers=self.num_hidden_layers							,							num_decoder_layers=self.decoder_layers							,							num_heads=self.num_attention_heads							,							relative_attention_num_buckets=self.relative_attention_num_buckets							,							dropout_rate=self.dropout_rate							,							initializer_factor=self.initializer_factor							,							eos_token_id=self.eos_token_id							,							bos_token_id=self.pad_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.decoder_start_token_id							,							)




					def 			snake_case__  ( self):
								return TaConfig(
								    vocab_size=self.vocab_size							,							d_model=self.hidden_size							,							d_ff=self.d_ff							,							d_kv=self.hidden_size // self.num_attention_heads							,							num_layers=self.num_hidden_layers							,							num_decoder_layers=self.decoder_layers							,							num_heads=self.num_attention_heads							,							relative_attention_num_buckets=self.relative_attention_num_buckets							,							dropout_rate=self.dropout_rate							,							initializer_factor=self.initializer_factor							,							eos_token_id=self.eos_token_id							,							bos_token_id=self.pad_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.decoder_start_token_id							,							)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							):
								UpperCAmelCase__					:   Dict       				=       UMTaModel(config=_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()
								UpperCAmelCase__					:   Dict       				=       model(
								    input_ids=_lowerCamelCase							,							decoder_input_ids=_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							decoder_attention_mask=_lowerCamelCase							,							)
								UpperCAmelCase__					:   List[str]       				=       model(input_ids=_lowerCamelCase							,							decoder_input_ids=_lowerCamelCase)
								UpperCAmelCase__					:   Tuple       				=       result.last_hidden_state
								UpperCAmelCase__					:   List[str]       				=       result.past_key_values
								UpperCAmelCase__					:   List[str]       				=       result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size()							,							(self.batch_size, self.encoder_seq_length, self.hidden_size))
								self.parent.assertEqual(decoder_output.size()							,							(self.batch_size, self.decoder_seq_length, self.hidden_size))
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(_lowerCamelCase)							,							config.num_layers)
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0])							,							4)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[Any]       				=       UMTaModel(config=_lowerCamelCase).get_decoder().to(_lowerCamelCase).eval()
								# first forward pass
								UpperCAmelCase__					:   Dict       				=       model(_lowerCamelCase							,							use_cache=_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       model(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       model(_lowerCamelCase							,							use_cache=_lowerCamelCase)

								self.parent.assertTrue(len(_lowerCamelCase) == len(_lowerCamelCase))
								self.parent.assertTrue(len(_lowerCamelCase) == len(_lowerCamelCase) + 1)

								UpperCAmelCase__					:   List[str]       				=       outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase__					:   Any       				=       ids_tensor((self.batch_size, 1)							,							config.vocab_size)

								# append to next input_ids and
								UpperCAmelCase__					:   Optional[Any]       				=       torch.cat([input_ids, next_tokens]							,							dim=-1)

								UpperCAmelCase__					:   Optional[int]       				=       model(_lowerCamelCase)["""last_hidden_state"""]
								UpperCAmelCase__					:   int       				=       model(_lowerCamelCase							,							past_key_values=_lowerCamelCase)["""last_hidden_state"""]

								# select random slice
								UpperCAmelCase__					:   Any       				=       ids_tensor((1,)							,							output_from_past.shape[-1]).item()
								UpperCAmelCase__					:   Optional[int]       				=       output_from_no_past[:, -1, random_slice_idx].detach()
								UpperCAmelCase__					:   int       				=       output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_lowerCamelCase							,							_lowerCamelCase							,							atol=1e-3))




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							):
								UpperCAmelCase__					:   str       				=       UMTaModel(config=_lowerCamelCase).to(_lowerCamelCase).half().eval()
								UpperCAmelCase__					:   Dict       				=       model(**_lowerCamelCase)["""last_hidden_state"""]
								self.parent.assertFalse(torch.isnan(_lowerCamelCase).any().item())


@require_torch
class 						_snake_case   (       a__    ,				a__    ,				a__    ,				unittest.TestCase		):
					lowerCAmelCase :str						=   (
					    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
					)
					lowerCAmelCase :Union[str, Any]						=   (UMTaForConditionalGeneration,) if is_torch_available() else ()
					lowerCAmelCase :str						=   (
					    {
					        '''conversational''': UMTaForConditionalGeneration,
					        '''feature-extraction''': UMTaModel,
					        '''summarization''': UMTaForConditionalGeneration,
					        '''text2text-generation''': UMTaForConditionalGeneration,
					        '''translation''': UMTaForConditionalGeneration,
					        '''question-answering''': UMTaForQuestionAnswering,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase :Union[str, Any]						=   True
					lowerCAmelCase :Any						=   False
					lowerCAmelCase :Optional[int]						=   False
					lowerCAmelCase :Optional[Any]						=   True
					lowerCAmelCase :List[Any]						=   True
					# The small UMT5 model needs higher percentages for CPU/MP tests
					lowerCAmelCase :str						=   [0.8, 0.9]




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       UMTaModelTester(self)




					@unittest.skip("""Test has a segmentation fault on torch 1.8.0""")
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase__					:   Optional[Any]       				=       UMTaModel(config_and_inputs[0]).to(_lowerCamelCase)
								with tempfile.TemporaryDirectory() as tmpdirname:
											torch.onnx.export(
											    _lowerCamelCase							,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							,							f'''{tmpdirname}/t5_test.onnx'''							,							export_params=_lowerCamelCase							,							opset_version=9							,							input_names=["""input_ids""", """decoder_input_ids"""]							,							)




					@unittest.skipIf(torch_device == """cpu"""							,							"""Cant do half precision""")
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
								UpperCAmelCase__					:   Tuple       				=       self.model_tester.prepare_config_and_inputs()
								UpperCAmelCase__					:   List[str]       				=       config_and_inputs[0]
								UpperCAmelCase__					:   str       				=       UMTaForConditionalGeneration(_lowerCamelCase).eval()
								model.to(_lowerCamelCase)

								UpperCAmelCase__					:   Dict       				=       {
								    """head_mask""": torch.zeros(config.num_layers							,							config.num_heads							,							device=_lowerCamelCase),
								    """decoder_head_mask""": torch.zeros(config.num_decoder_layers							,							config.num_heads							,							device=_lowerCamelCase),
								    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers							,							config.num_heads							,							device=_lowerCamelCase),
								}

								for attn_name, (name, mask) in zip(_lowerCamelCase							,							head_masking.items()):
											UpperCAmelCase__					:   List[Any]       				=       {name: mask}
											# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
											if name == "head_mask":
														UpperCAmelCase__					:   Any       				=       torch.ones(
														    config.num_decoder_layers							,							config.num_heads							,							device=_lowerCamelCase)

											UpperCAmelCase__					:   Optional[int]       				=       model.generate(
											    config_and_inputs[1]["""input_ids"""]							,							num_beams=1							,							max_length=3							,							output_attentions=_lowerCamelCase							,							return_dict_in_generate=_lowerCamelCase							,							**_lowerCamelCase							,							)
											# We check the state of decoder_attentions and cross_attentions just from the last step
											UpperCAmelCase__					:   Tuple       				=       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
											self.assertEqual(sum([w.sum().item() for w in attn_weights])							,							0.0)




					@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""")
					def 			snake_case__  ( self):
								pass


@require_torch
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					@unittest.skip(
					    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""")
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""							,							return_dict=_lowerCamelCase).to(_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       AutoTokenizer.from_pretrained("""google/umt5-small"""							,							use_fast=_lowerCamelCase							,							legacy=_lowerCamelCase)
								UpperCAmelCase__					:   Tuple       				=       [
								    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
								    """No se como puedo <extra_id_0>.""",
								    """This is the reason why we <extra_id_0> them.""",
								    """The <extra_id_0> walks in <extra_id_1>, seats""",
								    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
								]
								UpperCAmelCase__					:   str       				=       tokenizer(_lowerCamelCase							,							return_tensors="""pt"""							,							padding=_lowerCamelCase).input_ids
								# fmt: off
								UpperCAmelCase__					:   Optional[int]       				=       torch.tensor(
								    [
								        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
								    ])
								# fmt: on
								torch.testing.assert_allclose(_lowerCamelCase							,							_lowerCamelCase)

								UpperCAmelCase__					:   List[str]       				=       model.generate(input_ids.to(_lowerCamelCase))
								UpperCAmelCase__					:   Dict       				=       [
								    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
								    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								]
								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer.batch_decode(_lowerCamelCase)
								self.assertEqual(_lowerCamelCase							,							_lowerCamelCase)



'''simple docstring'''



import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)


class 						_snake_case   (       unittest.TestCase		):




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       Vector([1, 2, 3])
								self.assertEqual(x.component(0)							,							1)
								self.assertEqual(x.component(2)							,							3)
								UpperCAmelCase__					:   List[str]       				=       Vector()




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       Vector([0, 0, 0, 0, 0, 1])
								self.assertEqual(str(_lowerCamelCase)							,							"""(0,0,0,0,0,1)""")




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       Vector([1, 2, 3, 4])
								self.assertEqual(len(_lowerCamelCase)							,							4)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       Vector([1, 2])
								UpperCAmelCase__					:   Optional[int]       				=       Vector([1, 2, 3, 4, 5])
								UpperCAmelCase__					:   Optional[Any]       				=       Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
								UpperCAmelCase__					:   Union[str, Any]       				=       Vector([1, -1, 1, -1, 2, -3, 4, -5])
								self.assertAlmostEqual(x.euclidean_length()							,							2.236							,							3)
								self.assertAlmostEqual(y.euclidean_length()							,							7.416							,							3)
								self.assertEqual(z.euclidean_length()							,							0)
								self.assertAlmostEqual(w.euclidean_length()							,							7.616							,							3)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       Vector([1, 2, 3])
								UpperCAmelCase__					:   Optional[Any]       				=       Vector([1, 1, 1])
								self.assertEqual((x + y).component(0)							,							2)
								self.assertEqual((x + y).component(1)							,							3)
								self.assertEqual((x + y).component(2)							,							4)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       Vector([1, 2, 3])
								UpperCAmelCase__					:   Dict       				=       Vector([1, 1, 1])
								self.assertEqual((x - y).component(0)							,							0)
								self.assertEqual((x - y).component(1)							,							1)
								self.assertEqual((x - y).component(2)							,							2)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Tuple       				=       Vector([1, 2, 3])
								UpperCAmelCase__					:   Optional[int]       				=       Vector([2, -1, 4])  # for test of dot product
								UpperCAmelCase__					:   Any       				=       Vector([1, -2, -1])
								self.assertEqual(str(x * 3.0)							,							"""(3.0,6.0,9.0)""")
								self.assertEqual((a * b)							,							0)




					def 			snake_case__  ( self):
								self.assertEqual(str(zero_vector(10)).count("""0""")							,							10)




					def 			snake_case__  ( self):
								self.assertEqual(str(unit_basis_vector(3							,							1))							,							"""(0,1,0)""")




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       Vector([1, 2, 3])
								UpperCAmelCase__					:   List[str]       				=       Vector([1, 0, 1])
								self.assertEqual(str(axpy(2							,							_lowerCamelCase							,							_lowerCamelCase))							,							"""(3,4,7)""")




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       Vector([1, 0, 0, 0, 0, 0])
								UpperCAmelCase__					:   Optional[int]       				=       x.copy()
								self.assertEqual(str(_lowerCamelCase)							,							str(_lowerCamelCase))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       Vector([1, 0, 0])
								x.change_component(0							,							0)
								x.change_component(1							,							1)
								self.assertEqual(str(_lowerCamelCase)							,							"""(0,1,0)""")




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""							,							str(_lowerCamelCase))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								UpperCAmelCase__					:   Dict       				=       [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
								for x in range(a.height()):
											for y in range(a.width()):
														self.assertEqual(minors[x][y]							,							a.minor(_lowerCamelCase							,							_lowerCamelCase))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								UpperCAmelCase__					:   Tuple       				=       [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
								for x in range(a.height()):
											for y in range(a.width()):
														self.assertEqual(cofactors[x][y]							,							a.cofactor(_lowerCamelCase							,							_lowerCamelCase))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								self.assertEqual(-5							,							a.determinant())




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]							,							3							,							3)
								UpperCAmelCase__					:   List[Any]       				=       Vector([1, 2, 3])
								self.assertEqual("""(14,32,50)"""							,							str(a * x))
								self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""							,							str(a * 2))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								a.change_component(0							,							2							,							5)
								self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""							,							str(_lowerCamelCase))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								self.assertEqual(7							,							a.component(2							,							1)							,							0.01)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								UpperCAmelCase__					:   List[Any]       				=       Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]							,							3							,							3)
								self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""							,							str(a + b))




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]							,							3							,							3)
								UpperCAmelCase__					:   Dict       				=       Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]							,							3							,							3)
								self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""							,							str(a - b))




					def 			snake_case__  ( self):
								self.assertEqual(
								    """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""							,							str(square_zero_matrix(5))							,							)


if __name__ == "__main__":
		unittest.main()