"""simple docstring"""






from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
						from transformers import (
						    TFBertModel,
						    TFCLIPVisionModel,
						    TFDeiTModel,
						    TFRobertaModel,
						    TFVisionTextDualEncoderModel,
						    TFViTModel,
						    VisionTextDualEncoderConfig,
						)

if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor



def 						a_		(      lowerCamelCase ):
				if isinstance(lowerCamelCase      ,    collections.abc.Iterable ):
								return x
				return (x, x)
@require_tf
class        snake_case :




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]    ):
									pass







					def 	__lowerCAmelCase		(       self     :			str    ):
									pass







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									pass







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			List[str]=None					,**lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__					,lowerCamelCase__    )

									UpperCAmelCase__    =							TFVisionTextDualEncoderModel(lowerCamelCase__    )

									UpperCAmelCase__    =							model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__    )

									self.assertEqual(output['text_embeds'].shape					,(input_ids.shape[0], config.projection_dim)    )
									self.assertEqual(output['image_embeds'].shape					,(pixel_values.shape[0], config.projection_dim)    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[int]=None					,**lowerCamelCase__     :			Any    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel(vision_model=lowerCamelCase__					,text_model=lowerCamelCase__    )

									UpperCAmelCase__    =							model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__    )

									self.assertEqual(output['text_embeds'].shape					,(input_ids.shape[0], model.config.projection_dim)    )
									self.assertEqual(output['image_embeds'].shape					,(pixel_values.shape[0], model.config.projection_dim)    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			int					,lowerCamelCase__     :			str					,lowerCamelCase__     :			List[str]=None					,**lowerCamelCase__     :			Union[str, Any]    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							{'vision_model': vision_model, 'text_model': text_model}
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__    )

									UpperCAmelCase__    =							model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__    )

									self.assertEqual(output['text_embeds'].shape					,(input_ids.shape[0], model.config.projection_dim)    )
									self.assertEqual(output['image_embeds'].shape					,(pixel_values.shape[0], model.config.projection_dim)    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]=None					,**lowerCamelCase__     :			Union[str, Any]    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel(vision_model=lowerCamelCase__					,text_model=lowerCamelCase__    )

									UpperCAmelCase__    =							model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__    )
									UpperCAmelCase__    =							output[0].numpy()

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__    )

													UpperCAmelCase__    =							model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__    )
													UpperCAmelCase__    =							after_output[0].numpy()
													UpperCAmelCase__    =							np.amax(np.abs(out_a - out_a    )    )
													self.assertLessEqual(lowerCamelCase__					,1e-5    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]=None					,**lowerCamelCase__     :			Dict    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel(vision_model=lowerCamelCase__					,text_model=lowerCamelCase__    )

									UpperCAmelCase__    =							model(
									    input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__					,output_attentions=lowerCamelCase__    )

									UpperCAmelCase__    =							output.vision_model_output.attentions
									self.assertEqual(len(lowerCamelCase__    )					,vision_config.num_hidden_layers    )

									# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
									UpperCAmelCase__    =							to_atuple(vision_model.config.image_size    )
									UpperCAmelCase__    =							to_atuple(vision_model.config.patch_size    )
									UpperCAmelCase__    =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									UpperCAmelCase__    =							num_patches + 1
									self.assertEqual(vision_attentions[0].shape[-3:]					,(vision_config.num_attention_heads, seq_len, seq_len)    )

									UpperCAmelCase__    =							output.text_model_output.attentions
									self.assertEqual(len(lowerCamelCase__    )					,text_config.num_hidden_layers    )

									self.assertEqual(
									    text_attentions[0].shape[-3:]					,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,)







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			float    ):
									UpperCAmelCase__    =							np.abs((a - b)    ).max()
									self.assertLessEqual(lowerCamelCase__					,lowerCamelCase__					,f'''Difference between torch and flax is {diff} (>= {tol}).'''    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_model(**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									self.check_save_load(**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_pretrained_model_and_inputs()

									UpperCAmelCase__    =							model_a(**lowerCamelCase__    )
									UpperCAmelCase__    =							outputs[0].numpy()

									with tempfile.TemporaryDirectory() as tmp_dirname:
													model_a.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__    )

													UpperCAmelCase__    =							model_a(**lowerCamelCase__    )
													UpperCAmelCase__    =							after_outputs[0].numpy()
													UpperCAmelCase__    =							np.amax(np.abs(out_a - out_a    )    )
													self.assertLessEqual(lowerCamelCase__					,1e-5    )






@require_tf
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    'hf-internal-testing/tiny-random-vit'					,'hf-internal-testing/tiny-random-bert'    )
									UpperCAmelCase__    =							13
									UpperCAmelCase__    =							floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]    )
									UpperCAmelCase__    =							ids_tensor([batch_size, 4]					,model.text_model.config.vocab_size    )
									UpperCAmelCase__    =							random_attention_mask([batch_size, 4]    )
									UpperCAmelCase__    =							{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

									return model, inputs







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							TFViTModel(lowerCamelCase__					,name='vision_model'    )
									UpperCAmelCase__    =							TFBertModel(lowerCamelCase__					,name='text_model'    )
									return vision_model, text_model







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							TFViTModelTester(self    )
									UpperCAmelCase__    =							TFBertModelTester(self    )
									UpperCAmelCase__    =							vit_model_tester.prepare_config_and_inputs()
									UpperCAmelCase__    =							bert_model_tester.prepare_config_and_inputs()

									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							vision_config_and_inputs

									(
									    (
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			
									)    =							text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}






@require_tf
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			int    ):
									# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
									# just reinitialize it.
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    'Rocketknight1/tiny-random-deit-tf'					,'hf-internal-testing/tiny-random-roberta'    )
									UpperCAmelCase__    =							13
									UpperCAmelCase__    =							floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]    )
									UpperCAmelCase__    =							ids_tensor([batch_size, 4]					,model.text_model.config.vocab_size    )
									UpperCAmelCase__    =							random_attention_mask([batch_size, 4]    )
									UpperCAmelCase__    =							{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

									return model, inputs







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Tuple=None					,**lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel(vision_model=lowerCamelCase__					,text_model=lowerCamelCase__    )

									UpperCAmelCase__    =							model(
									    input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__					,output_attentions=lowerCamelCase__    )

									UpperCAmelCase__    =							output.vision_model_output.attentions
									self.assertEqual(len(lowerCamelCase__    )					,vision_config.num_hidden_layers    )

									# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
									UpperCAmelCase__    =							to_atuple(vision_model.config.image_size    )
									UpperCAmelCase__    =							to_atuple(vision_model.config.patch_size    )
									UpperCAmelCase__    =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									UpperCAmelCase__    =							num_patches + 2
									self.assertEqual(vision_attentions[0].shape[-3:]					,(vision_config.num_attention_heads, seq_len, seq_len)    )

									UpperCAmelCase__    =							output.text_model_output.attentions
									self.assertEqual(len(lowerCamelCase__    )					,text_config.num_hidden_layers    )

									self.assertEqual(
									    text_attentions[0].shape[-3:]					,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,)







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Dict    ):
									UpperCAmelCase__    =							TFDeiTModel(lowerCamelCase__					,name='vision_model'    )
									UpperCAmelCase__    =							TFRobertaModel(lowerCamelCase__					,name='text_model'    )
									return vision_model, text_model







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							TFDeiTModelTester(self    )
									UpperCAmelCase__    =							TFRobertaModelTester(self    )
									UpperCAmelCase__    =							vit_model_tester.prepare_config_and_inputs()
									UpperCAmelCase__    =							bert_model_tester.prepare_config_and_inputs()

									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							vision_config_and_inputs

									(
									    (
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			
									)    =							text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}






@require_tf
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    'Rocketknight1/tiny-random-clip-tf'					,'hf-internal-testing/tiny-random-bert'    )
									UpperCAmelCase__    =							13
									UpperCAmelCase__    =							floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]    )
									UpperCAmelCase__    =							ids_tensor([batch_size, 4]					,model.text_model.config.vocab_size    )
									UpperCAmelCase__    =							random_attention_mask([batch_size, 4]    )
									UpperCAmelCase__    =							{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

									return model, inputs







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							TFCLIPVisionModel(lowerCamelCase__					,name='vision_model'    )
									UpperCAmelCase__    =							TFBertModel(lowerCamelCase__					,name='text_model'    )
									return vision_model, text_model







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							TFCLIPVisionModelTester(self    )
									UpperCAmelCase__    =							TFBertModelTester(self    )
									UpperCAmelCase__    =							clip_model_tester.prepare_config_and_inputs()
									UpperCAmelCase__    =							bert_model_tester.prepare_config_and_inputs()

									UpperCAmelCase__   ,			UpperCAmelCase__    =							vision_config_and_inputs

									(
									    (
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			(
									    UpperCAmelCase__
									)   ,			
									)    =							text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}






@require_vision
@require_tf
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					@slow
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							TFVisionTextDualEncoderModel.from_pretrained(
									    'clip-italian/clip-italian'					,logit_scale_init_value=1.0					,from_pt=lowerCamelCase__    )
									UpperCAmelCase__    =							VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'    )

									UpperCAmelCase__    =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
									UpperCAmelCase__    =							processor(
									    text=['una foto di un gatto', 'una foto di un cane']					,images=lowerCamelCase__					,padding=lowerCamelCase__					,return_tensors='np'    )

									UpperCAmelCase__    =							model(**lowerCamelCase__    )

									# verify the logits
									self.assertEqual(outputs.logits_per_image.shape					,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])    )
									self.assertEqual(
									    outputs.logits_per_text.shape					,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])					,)

									UpperCAmelCase__    =							np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]    )

									self.assertTrue(np.allclose(outputs.logits_per_image.numpy()					,lowerCamelCase__					,atol=1e-3    )    )




"""simple docstring"""






from __future__ import annotations
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			Dict					,lowerCamelCase__     :			list[list[int]]    ):
									UpperCAmelCase__    =							TypeError(
									    'Matrices must be formed from a list of zero or more lists containing at '
									    'least one and the same number of values, each of which must be of type '
									    'int or float.'    )
									if len(lowerCamelCase__    ) != 0:
													UpperCAmelCase__    =							len(rows[0]    )
													if cols == 0:
																	raise error
													for row in rows:
																	if len(lowerCamelCase__    ) != cols:
																					raise error
																	for value in row:
																					if not isinstance(lowerCamelCase__					,(int, float)    ):
																									raise error
													UpperCAmelCase__    =							rows
									else:
													UpperCAmelCase__    =							[]







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									return [[row[i] for row in self.rows] for i in range(len(self.rows[0]    )    )]







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									return len(self.rows    )







					@property
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return len(self.rows[0]    )







					@property
					def 	__lowerCAmelCase		(       self     :			Any    ):
									return (self.num_rows, self.num_columns)







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									return self.order[0] == self.order[1]







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							[
									    [0 if column_num != row_num else 1 for column_num in range(self.num_rows    )]
									    for row_num in range(self.num_rows    )
									]
									return Matrix(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									if not self.is_square:
													return 0
									if self.order == (0, 0):
													return 1
									if self.order == (1, 1):
													return int(self.rows[0][0]    )
									if self.order == (2, 2):
													return int(
													    (self.rows[0][0] * self.rows[1][1])
													    - (self.rows[0][1] * self.rows[1][0])    )
									else:
													return sum(
													    self.rows[0][column] * self.cofactors().rows[0][column]
													    for column in range(self.num_columns    )    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									return bool(self.determinant()    )







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							[
									    [
									        self.rows[other_row][other_column]
									        for other_column in range(self.num_columns    )
									        if other_column != column
									    ]
									    for other_row in range(self.num_rows    )
									    if other_row != row
									]
									return Matrix(lowerCamelCase__    ).determinant()







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int    ):
									if (row + column) % 2 == 0:
													return self.get_minor(lowerCamelCase__					,lowerCamelCase__    )
									return -1 * self.get_minor(lowerCamelCase__					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									return Matrix(
									    [
									        [self.get_minor(lowerCamelCase__					,lowerCamelCase__    ) for column in range(self.num_columns    )]
									        for row in range(self.num_rows    )
									    ]    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									return Matrix(
									    [
									        [
									            self.minors().rows[row][column]
									            if (row + column) % 2 == 0
									            else self.minors().rows[row][column] * -1
									            for column in range(self.minors().num_columns    )
									        ]
									        for row in range(self.minors().num_rows    )
									    ]    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							[
									    [self.cofactors().rows[column][row] for column in range(self.num_columns    )]
									    for row in range(self.num_rows    )
									]
									return Matrix(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							self.determinant()
									if not determinant:
													raise TypeError('Only matrices with a non-zero determinant have an inverse'    )
									return self.adjugate() * (1 / determinant)







					def __repr__(       self     :			Optional[Any]    ):
									return str(self.rows    )







					def __str__(       self     :			List[str]    ):
									if self.num_rows == 0:
													return "[]"
									if self.num_rows == 1:
													return "[[" + ". ".join(str(self.rows[0]    )    ) + "]]"
									return (
									    "["
									    + "\n ".join(
									        [
									            '[' + '. '.join([str(lowerCamelCase__    ) for value in row]    ) + '.]'
									            for row in self.rows
									        ]    )
									    + "]"
									)







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			int | None = None    ):
									UpperCAmelCase__    =							TypeError('Row must be a list containing all ints and/or floats'    )
									if not isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													raise type_error
									for value in row:
													if not isinstance(lowerCamelCase__					,(int, float)    ):
																	raise type_error
									if len(lowerCamelCase__    ) != self.num_columns:
													raise ValueError(
													    'Row must be equal in length to the other rows in the matrix'    )
									if position is None:
													self.rows.append(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							self.rows[0:position] + [row] + self.rows[position:]







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			int | None = None    ):
									UpperCAmelCase__    =							TypeError(
									    'Column must be a list containing all ints and/or floats'    )
									if not isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													raise type_error
									for value in column:
													if not isinstance(lowerCamelCase__					,(int, float)    ):
																	raise type_error
									if len(lowerCamelCase__    ) != self.num_rows:
													raise ValueError(
													    'Column must be equal in length to the other columns in the matrix'    )
									if position is None:
													UpperCAmelCase__    =							[self.rows[i] + [column[i]] for i in range(self.num_rows    )]
									else:
													UpperCAmelCase__    =							[
													    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
													    for i in range(self.num_rows    )
													]







					def __eq__(       self     :			List[Any]					,lowerCamelCase__     :			object    ):
									if not isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													return NotImplemented
									return self.rows == other.rows







					def __ne__(       self     :			Any					,lowerCamelCase__     :			object    ):
									return not self == other







					def __neg__(       self     :			Dict    ):
									return self * -1







					def __add__(       self     :			str					,lowerCamelCase__     :			Matrix    ):
									if self.order != other.order:
													raise ValueError('Addition requires matrices of the same order'    )
									return Matrix(
									    [
									        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns    )]
									        for i in range(self.num_rows    )
									    ]    )







					def __sub__(       self     :			List[str]					,lowerCamelCase__     :			Matrix    ):
									if self.order != other.order:
													raise ValueError('Subtraction requires matrices of the same order'    )
									return Matrix(
									    [
									        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns    )]
									        for i in range(self.num_rows    )
									    ]    )







					def __mul__(       self     :			List[str]					,lowerCamelCase__     :			Matrix | int | float    ):
									if isinstance(lowerCamelCase__					,(int, float)    ):
													return Matrix(
													    [[int(element * other    ) for element in row] for row in self.rows]    )
									elif isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													if self.num_columns != other.num_rows:
																	raise ValueError(
																	    'The number of columns in the first matrix must '
																	    'be equal to the number of rows in the second'    )
													return Matrix(
													    [
													        [Matrix.dot_product(lowerCamelCase__					,lowerCamelCase__    ) for column in other.columns()]
													        for row in self.rows
													    ]    )
									else:
													raise TypeError(
													    'A Matrix can only be multiplied by an int, float, or another matrix'    )







					def __pow__(       self     :			Optional[int]					,lowerCamelCase__     :			int    ):
									if not isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													raise TypeError('A Matrix can only be raised to the power of an int'    )
									if not self.is_square:
													raise ValueError('Only square matrices can be raised to a power'    )
									if other == 0:
													return self.identity()
									if other < 0:
													if self.is_invertable():
																	return self.inverse() ** (-other)
													raise ValueError(
													    'Only invertable matrices can be raised to a negative power'    )
									UpperCAmelCase__    =							self
									for _ in range(other - 1    ):
													result *= self
									return result







					@classmethod
					def 	__lowerCAmelCase		(       cls     :			Union[str, Any]					,lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									return sum(row[i] * column[i] for i in range(len(lowerCamelCase__    )    )    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



