

'''simple docstring'''





import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)



def 					_lowercase						(   lowerCamelCase__			:		int							):







				if model.config.model_type == "gpt2":
								return model.transformer.h[0].mlp.c_fc
				return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
							import torch
							import torch.nn as nn
							class        A  (	nn.Module ):





													def __init__(	self	,    snake_case_	,    snake_case_			)			->							Union[str, Any]:
																	super().__init__()
																	_a        =   module
																	_a        =   nn.Sequential(
																	    nn.Linear(module.in_features	,    snake_case_	,    bias=snake_case_			)	,    nn.Linear(snake_case_	,    module.out_features	,    bias=snake_case_			)	,    )
																	_a        =   (2.0 / (5 * min(module.in_features	,    module.out_features			))) ** 0.5
																	nn.init.normal_(self.adapter[0].weight	,    std=snake_case_			)
																	nn.init.zeros_(self.adapter[1].weight			)
																	self.adapter.to(module.weight.device			)





													def __lowerCAmelCase   (	self	,    snake_case_	,    *snake_case_	,    **snake_case_			)			->							Union[str, Any]:
																	return self.module(snake_case_	,    *snake_case_	,    **snake_case_			) + self.adapter(snake_case_			)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        A  (	unittest.TestCase ):
						# We keep the constants inside the init function and model loading inside setUp function

						# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
						# Therefore here we use only bloom-1b3 to test our module
						__UpperCAmelCase				:	Dict   =   'bigscience/bloom-1b7'

						# Constant values
						__UpperCAmelCase				:	Union[str, Any]   =   2.109_659_552_692_574

						__UpperCAmelCase				:	List[str]   =   'Hello my name is'
						__UpperCAmelCase				:	Union[str, Any]   =   set()
						EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
						EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
						EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
						__UpperCAmelCase				:	Any   =   10





						def __lowerCAmelCase   (	self			)			->							Any:
										_a        =   AutoTokenizer.from_pretrained(self.model_name			)



class        A  (	_snake_case ):





						def __lowerCAmelCase   (	self			)			->							List[str]:
										super().setUp()

										# Models and tokenizer
										_a        =   AutoModelForCausalLM.from_pretrained(
										    self.model_name	,    torch_dtype=torch.floataa	,    device_map="auto"			)
										_a        =   AutoModelForCausalLM.from_pretrained(self.model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)





						def __lowerCAmelCase   (	self			)			->							Tuple:
										del self.model_fpaa
										del self.model_abit

										gc.collect()
										torch.cuda.empty_cache()





						def __lowerCAmelCase   (	self			)			->							Any:
										_a        =   self.model_abit.config

										self.assertTrue(hasattr(snake_case_	,    "quantization_config"			)			)

										_a        =   config.to_dict()
										_a        =   config.to_diff_dict()

										_a        =   config.to_json_string()





						def __lowerCAmelCase   (	self			)			->							str:
										from bitsandbytes.nn import Paramsabit

										_a        =   self.model_fpaa.get_memory_footprint()
										_a        =   self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit	,    self.EXPECTED_RELATIVE_DIFFERENCE			)
										_a        =   get_some_linear_layer(self.model_abit			)
										self.assertTrue(linear.weight.__class__ == Paramsabit			)





						def __lowerCAmelCase   (	self			)			->							Any:
										from transformers import TaPreTrainedModel

										self.model_fpaa.get_memory_footprint()
										self.model_abit.get_memory_footprint()

										for name, module in self.model_abit.named_modules():
														if isinstance(snake_case_	,    torch.nn.Linear			):
																		if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																						# 4-bit parameters are packed in uint8 variables
																						self.assertTrue(module.weight.dtype == torch.uinta			)





						def __lowerCAmelCase   (	self			)			->							str:
										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			)
										_a        =   self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0			)	,    max_new_tokens=1_0			)

										self.assertIn(self.tokenizer.decode(output_sequences[0]	,    skip_special_tokens=snake_case_			)	,    self.EXPECTED_OUTPUTS			)





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   BitsAndBytesConfig()
										_a        =   True

										_a        =   AutoModelForCausalLM.from_pretrained(
										    self.model_name	,    quantization_config=snake_case_	,    device_map="auto"			)

										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			)
										_a        =   model_abit_from_config.generate(
										    input_ids=encoded_input["input_ids"].to(0			)	,    max_new_tokens=1_0			)

										self.assertIn(self.tokenizer.decode(output_sequences[0]	,    skip_special_tokens=snake_case_			)	,    self.EXPECTED_OUTPUTS			)





						def __lowerCAmelCase   (	self			)			->							Tuple:

										with self.assertRaises(snake_case_			), tempfile.TemporaryDirectory() as tmpdirname:
														self.model_abit.save_pretrained(snake_case_			)





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   BitsAndBytesConfig()

										with self.assertRaises(snake_case_			):
														_a        =   AutoModelForCausalLM.from_pretrained(
														    self.model_name	,    quantization_config=snake_case_	,    load_in_abit=snake_case_	,    device_map="auto"	,    bnb_abit_quant_type="nf4"	,    )





						def __lowerCAmelCase   (	self			)			->							List[Any]:

										with self.assertRaises(snake_case_			):
														# Tries with `str`
														self.model_abit.to("cpu"			)

										with self.assertRaises(snake_case_			):
														# Tries with a `dtype``
														self.model_abit.to(torch.floataa			)

										with self.assertRaises(snake_case_			):
														# Tries with a `device`
														self.model_abit.to(torch.device("cuda:0"			)			)

										with self.assertRaises(snake_case_			):
														# Tries with a `device`
														self.model_abit.float()

										with self.assertRaises(snake_case_			):
														# Tries with a `device`
														self.model_abit.half()

										# Test if we did not break anything
										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			)

										_a        =   self.model_fpaa.to(torch.floataa			)
										_a        =   self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0			)	,    max_new_tokens=1_0			)

										# Check this does not throw an error
										_a        =   self.model_fpaa.to("cpu"			)

										# Check this does not throw an error
										_a        =   self.model_fpaa.half()

										# Check this does not throw an error
										_a        =   self.model_fpaa.float()





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   AutoModelForSeqaSeqLM.from_pretrained("t5-small"	,    load_in_abit=snake_case_	,    device_map="auto"			)
										self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa			)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        A  (	unittest.TestCase ):





						@classmethod
						def __lowerCAmelCase   (	cls			)			->							int:
										_a        =   "t5-small"
										_a        =   "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
										_a        =   AutoTokenizer.from_pretrained(cls.model_name			)
										_a        =   "Translate in German: Hello, my dog is cute"





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										gc.collect()
										torch.cuda.empty_cache()





						def __lowerCAmelCase   (	self			)			->							Tuple:
										from transformers import TaForConditionalGeneration

										_a        =   TaForConditionalGeneration._keep_in_fpaa_modules
										_a        =   None

										# test with `t5-small`
										_a        =   TaForConditionalGeneration.from_pretrained(self.model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)
										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			).to(0			)
										_a        =   model.generate(**snake_case_			)

										# test with `flan-t5-small`
										_a        =   TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)
										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			).to(0			)
										_a        =   model.generate(**snake_case_			)
										_a        =   modules





						def __lowerCAmelCase   (	self			)			->							List[Any]:
										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										_a        =   TaForConditionalGeneration.from_pretrained(self.model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)

										# there was a bug with decoders - this test checks that it is fixed
										self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q	,    bnb.nn.Linearabit			)			)

										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			).to(0			)
										_a        =   model.generate(**snake_case_			)

										# test with `flan-t5-small`
										_a        =   TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)
										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			).to(0			)
										_a        =   model.generate(**snake_case_			)



class        A  (	_snake_case ):





						def __lowerCAmelCase   (	self			)			->							List[Any]:
										super().setUp()
										# model_name
										_a        =   "bigscience/bloom-560m"
										_a        =   "t5-small"

										# Different types of model

										_a        =   AutoModel.from_pretrained(self.model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)
										# Sequence classification model
										_a        =   AutoModelForSequenceClassification.from_pretrained(
										    self.model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)
										# CausalLM model
										_a        =   AutoModelForCausalLM.from_pretrained(self.model_name	,    load_in_abit=snake_case_	,    device_map="auto"			)
										# Seq2seq model
										_a        =   AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name	,    load_in_abit=snake_case_	,    device_map="auto"			)





						def __lowerCAmelCase   (	self			)			->							int:
										del self.base_model
										del self.sequence_model
										del self.model_abit
										del self.seq_to_seq_model

										gc.collect()
										torch.cuda.empty_cache()





						def __lowerCAmelCase   (	self			)			->							List[str]:
										from bitsandbytes.nn import Paramsabit

										self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit			)

										# Other heads should be nn.Parameter
										self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter			)
										self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter			)
										self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter			)



class        A  (	_snake_case ):





						def __lowerCAmelCase   (	self			)			->							str:
										super().setUp()





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										del self.pipe

										gc.collect()
										torch.cuda.empty_cache()





						def __lowerCAmelCase   (	self			)			->							List[Any]:
										_a        =   pipeline(
										    "text-generation"	,    model=self.model_name	,    model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}	,    max_new_tokens=self.MAX_NEW_TOKENS	,    )

										# Real second forward pass
										_a        =   self.pipe(self.input_text			)
										self.assertIn(pipeline_output[0]["generated_text"]	,    self.EXPECTED_OUTPUTS			)



@require_torch_multi_gpu
class        A  (	_snake_case ):





						def __lowerCAmelCase   (	self			)			->							str:
										super().setUp()





						def __lowerCAmelCase   (	self			)			->							List[Any]:
										_a        =   AutoModelForCausalLM.from_pretrained(
										    self.model_name	,    load_in_abit=snake_case_	,    device_map="balanced"			)

										# Check correct device map
										self.assertEqual(set(model_parallel.hf_device_map.values()			)	,    {0, 1}			)

										# Check that inference pass works on the model
										_a        =   self.tokenizer(self.input_text	,    return_tensors="pt"			)

										# Second real batch
										_a        =   model_parallel.generate(input_ids=encoded_input["input_ids"].to(0			)	,    max_new_tokens=1_0			)
										self.assertIn(self.tokenizer.decode(output_parallel[0]	,    skip_special_tokens=snake_case_			)	,    self.EXPECTED_OUTPUTS			)



class        A  (	_snake_case ):





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   "facebook/opt-350m"
										super().setUp()





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

										if version.parse(importlib.metadata.version("bitsandbytes"			)			) < version.parse("0.37.0"			):
														return

										# Step 1: freeze all parameters
										_a        =   AutoModelForCausalLM.from_pretrained(self.model_name	,    load_in_abit=snake_case_			)

										self.assertEqual(set(model.hf_device_map.values()			)	,    {torch.cuda.current_device()}			)

										for param in model.parameters():
														_a        =   False  # freeze the model - train adapters later
														if param.ndim == 1:
																		# cast the small parameters (e.g. layernorm) to fp32 for stability
																		_a        =   param.data.to(torch.floataa			)

        # Step 2: add adapters
										for _, module in model.named_modules():
														if "OPTAttention" in repr(type(snake_case_			)			):
																		_a        =   LoRALayer(module.q_proj	,    rank=1_6			)
																		_a        =   LoRALayer(module.k_proj	,    rank=1_6			)
																		_a        =   LoRALayer(module.v_proj	,    rank=1_6			)

        # Step 3: dummy batch
										_a        =   self.tokenizer("Test batch "	,    return_tensors="pt"			).to(0			)

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
														_a        =   model.forward(**snake_case_			)
														out.logits.norm().backward()

										for module in model.modules():
														if isinstance(snake_case_	,    snake_case_			):
																		self.assertTrue(module.adapter[1].weight.grad is not None			)
																		self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0			)
														elif isinstance(snake_case_	,    nn.Embedding			):
																		self.assertTrue(module.weight.grad is None			)



class        A  (	_snake_case ):
						__UpperCAmelCase				:	Tuple   =   'gpt2-xl'
						__UpperCAmelCase				:	Dict   =   3.3_191_854_854_152_187












'''simple docstring'''

from __future__ import annotations







class __a			:





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      list[list[int]]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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:
         __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							rows
      else:
         __SCREAMING_SNAKE_CASE   				=							[]





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]     )     )]





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return len(self.rows     )





   @property
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      return len(self.rows[0]     )





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return (self.num_rows, self.num_columns)





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return self.order[0] == self.order[1]





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [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   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      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   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return bool(self.determinant()     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [
              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   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if (row + column) % 2 == 0:
         return self.get_minor(lowerCamelCase					,lowerCamelCase     )
      return -1 * self.get_minor(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return Matrix(
          [
              [self.get_minor(lowerCamelCase					,lowerCamelCase     ) for column in range(self.num_columns     )]
              for row in range(self.num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      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   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
          for row in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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					:      List[Any]     ):

      '''simple docstring'''

      return str(self.rows     )





   def __str__(       self					:      List[str]     ):

      '''simple docstring'''

      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   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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:
         __SCREAMING_SNAKE_CASE   				=							self.rows[0:position] + [row] + self.rows[position:]





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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:
         __SCREAMING_SNAKE_CASE   				=							[self.rows[i] + [column[i]] for i in range(self.num_rows     )]
      else:
         __SCREAMING_SNAKE_CASE   				=							[
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows     )
         ]





   def __eq__(       self					:      int					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         return NotImplemented
      return self.rows == other.rows





   def __ne__(       self					:      Any					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      return not self == other





   def __neg__(       self					:      Any     ):

      '''simple docstring'''

      return self * -1





   def __add__(       self					:      List[Any]					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      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					:      Any					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      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					:      Any					,lowerCamelCase					:      Matrix | int | float     ):

      '''simple docstring'''

      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     ):

      '''simple docstring'''

      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"""     )
      __SCREAMING_SNAKE_CASE   				=							self
      for _ in range(other - 1     ):
         result *= self
      return result





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      list[int]     ):

      '''simple docstring'''

      return sum(row[i] * column[i] for i in range(len(lowerCamelCase     )     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





