





'''simple docstring'''

import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
class __a			( unittest.TestCase      ):
   __UpperCamelCase :			Union[str, Any]						   =						MODEL_FOR_CAUSAL_LM_MAPPING
   __UpperCamelCase :			Tuple						   =						TF_MODEL_FOR_CAUSAL_LM_MAPPING





   @require_torch
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""text-generation"""					,model="""sshleifer/tiny-ctrl"""					,framework="""pt"""     )
      # Using `do_sample=False` to force deterministic output
      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""					,do_sample=lowerCamelCase     )
      self.assertEqual(
          lowerCamelCase					,[
              {
                  """generated_text""": (
                      """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                      """ oscope. FiliFili@@"""
                  )
              }
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							text_generator(["""This is a test""", """This is a second test"""]     )
      self.assertEqual(
          lowerCamelCase					,[
              [
                  {
                      """generated_text""": (
                          """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                          """ oscope. FiliFili@@"""
                      )
                  }
              ],
              [
                  {
                      """generated_text""": (
                          """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
                          """ oscope. oscope. FiliFili@@"""
                      )
                  }
              ],
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""					,do_sample=lowerCamelCase					,num_return_sequences=2					,return_tensors=lowerCamelCase     )
      self.assertEqual(
          lowerCamelCase					,[
              {"""generated_token_ids""": ANY(lowerCamelCase     )},
              {"""generated_token_ids""": ANY(lowerCamelCase     )},
          ]					,)
      __SCREAMING_SNAKE_CASE   				=							text_generator.model.config.eos_token_id
      __SCREAMING_SNAKE_CASE   				=							"""<pad>"""
      __SCREAMING_SNAKE_CASE   				=							text_generator(
          ["""This is a test""", """This is a second test"""]					,do_sample=lowerCamelCase					,num_return_sequences=2					,batch_size=2					,return_tensors=lowerCamelCase					,)
      self.assertEqual(
          lowerCamelCase					,[
              [
                  {"""generated_token_ids""": ANY(lowerCamelCase     )},
                  {"""generated_token_ids""": ANY(lowerCamelCase     )},
              ],
              [
                  {"""generated_token_ids""": ANY(lowerCamelCase     )},
                  {"""generated_token_ids""": ANY(lowerCamelCase     )},
              ],
          ]					,)





   @require_tf
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""text-generation"""					,model="""sshleifer/tiny-ctrl"""					,framework="""tf"""     )

      # Using `do_sample=False` to force deterministic output
      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""					,do_sample=lowerCamelCase     )
      self.assertEqual(
          lowerCamelCase					,[
              {
                  """generated_text""": (
                      """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                      """ please,"""
                  )
              }
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							text_generator(["""This is a test""", """This is a second test"""]					,do_sample=lowerCamelCase     )
      self.assertEqual(
          lowerCamelCase					,[
              [
                  {
                      """generated_text""": (
                          """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                          """ please,"""
                      )
                  }
              ],
              [
                  {
                      """generated_text""": (
                          """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
                          """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
                      )
                  }
              ],
          ]					,)





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TextGenerationPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase     )
      return text_generator, ["This is a test", "Another test"]





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""Hello I believe in"""
      __SCREAMING_SNAKE_CASE   				=							pipeline("""text-generation"""					,model="""hf-internal-testing/tiny-random-gpt2"""     )
      __SCREAMING_SNAKE_CASE   				=							text_generator(lowerCamelCase     )
      self.assertEqual(
          lowerCamelCase					,[{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]					,)

      __SCREAMING_SNAKE_CASE   				=							text_generator(lowerCamelCase					,stop_sequence=""" fe"""     )
      self.assertEqual(lowerCamelCase					,[{"""generated_text""": """Hello I believe in fe"""}]     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      str					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							text_generator.model
      __SCREAMING_SNAKE_CASE   				=							text_generator.tokenizer

      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""     )
      self.assertEqual(lowerCamelCase					,[{"""generated_text""": ANY(lowerCamelCase     )}]     )
      self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""     )     )

      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""					,return_full_text=lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,[{"""generated_text""": ANY(lowerCamelCase     )}]     )
      self.assertNotIn("""This is a test"""					,outputs[0]["""generated_text"""]     )

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""text-generation"""					,model=lowerCamelCase					,tokenizer=lowerCamelCase					,return_full_text=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""     )
      self.assertEqual(lowerCamelCase					,[{"""generated_text""": ANY(lowerCamelCase     )}]     )
      self.assertNotIn("""This is a test"""					,outputs[0]["""generated_text"""]     )

      __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test"""					,return_full_text=lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,[{"""generated_text""": ANY(lowerCamelCase     )}]     )
      self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""     )     )

      __SCREAMING_SNAKE_CASE   				=							text_generator(["""This is great !""", """Something else"""]					,num_return_sequences=2					,do_sample=lowerCamelCase     )
      self.assertEqual(
          lowerCamelCase					,[
              [{"""generated_text""": ANY(lowerCamelCase     )}, {"""generated_text""": ANY(lowerCamelCase     )}],
              [{"""generated_text""": ANY(lowerCamelCase     )}, {"""generated_text""": ANY(lowerCamelCase     )}],
          ]					,)

      if text_generator.tokenizer.pad_token is not None:
         __SCREAMING_SNAKE_CASE   				=							text_generator(
             ["""This is great !""", """Something else"""]					,num_return_sequences=2					,batch_size=2					,do_sample=lowerCamelCase     )
         self.assertEqual(
             lowerCamelCase					,[
                 [{"""generated_text""": ANY(lowerCamelCase     )}, {"""generated_text""": ANY(lowerCamelCase     )}],
                 [{"""generated_text""": ANY(lowerCamelCase     )}, {"""generated_text""": ANY(lowerCamelCase     )}],
             ]					,)

      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							text_generator("""test"""					,return_full_text=lowerCamelCase					,return_text=lowerCamelCase     )
      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							text_generator("""test"""					,return_full_text=lowerCamelCase					,return_tensors=lowerCamelCase     )
      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							text_generator("""test"""					,return_text=lowerCamelCase					,return_tensors=lowerCamelCase     )

      # Empty prompt is slighly special
      # it requires BOS token to exist.
      # Special case for Pegasus which will always append EOS so will
      # work even without BOS.
      if (
          text_generator.tokenizer.bos_token_id is not None
          or "Pegasus" in tokenizer.__class__.__name__
          or "Git" in model.__class__.__name__
      ):
         __SCREAMING_SNAKE_CASE   				=							text_generator(""""""     )
         self.assertEqual(lowerCamelCase					,[{"""generated_text""": ANY(lowerCamelCase     )}]     )
      else:
         with self.assertRaises((ValueError, AssertionError)     ):
            __SCREAMING_SNAKE_CASE   				=							text_generator(""""""     )

      if text_generator.framework == "tf":
         # TF generation does not support max_new_tokens, and it's impossible
         # to control long generation with only max_length without
         # fancy calculation, dismissing tests for now.
         return
      # We don't care about infinite range models.
      # They already work.
      # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
      __SCREAMING_SNAKE_CASE   				=							["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
      if (
          tokenizer.model_max_length < 1_0000
          and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
      ):
         # Handling of large generations
         with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)     ):
            text_generator("""This is a test""" * 500					,max_new_tokens=20     )

         __SCREAMING_SNAKE_CASE   				=							text_generator("""This is a test""" * 500					,handle_long_generation="""hole"""					,max_new_tokens=20     )
         # Hole strategy cannot work
         with self.assertRaises(lowerCamelCase     ):
            text_generator(
                """This is a test""" * 500					,handle_long_generation="""hole"""					,max_new_tokens=tokenizer.model_max_length + 10					,)





   @require_torch
   @require_accelerate
   @require_torch_gpu
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      import torch

      # Classic `model_kwargs`
      __SCREAMING_SNAKE_CASE   				=							pipeline(
          model="""hf-internal-testing/tiny-random-bloom"""					,model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa}					,)
      self.assertEqual(pipe.model.device					,torch.device(0     )     )
      self.assertEqual(pipe.model.lm_head.weight.dtype					,torch.bfloataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe("""This is a test"""     )
      self.assertEqual(
          lowerCamelCase					,[
              {
                  """generated_text""": (
                      """This is a test test test test test test test test test test test test test test test test"""
                      """ test"""
                  )
              }
          ]					,)

      # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
      __SCREAMING_SNAKE_CASE   				=							pipeline(model="""hf-internal-testing/tiny-random-bloom"""					,device_map="""auto"""					,torch_dtype=torch.bfloataa     )
      self.assertEqual(pipe.model.device					,torch.device(0     )     )
      self.assertEqual(pipe.model.lm_head.weight.dtype					,torch.bfloataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe("""This is a test"""     )
      self.assertEqual(
          lowerCamelCase					,[
              {
                  """generated_text""": (
                      """This is a test test test test test test test test test test test test test test test test"""
                      """ test"""
                  )
              }
          ]					,)

      # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
      __SCREAMING_SNAKE_CASE   				=							pipeline(model="""hf-internal-testing/tiny-random-bloom"""					,device_map="""auto"""     )
      self.assertEqual(pipe.model.device					,torch.device(0     )     )
      self.assertEqual(pipe.model.lm_head.weight.dtype					,torch.floataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe("""This is a test"""     )
      self.assertEqual(
          lowerCamelCase					,[
              {
                  """generated_text""": (
                      """This is a test test test test test test test test test test test test test test test test"""
                      """ test"""
                  )
              }
          ]					,)





   @require_torch
   @require_torch_gpu
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      import torch

      __SCREAMING_SNAKE_CASE   				=							pipeline(model="""hf-internal-testing/tiny-random-bloom"""					,device=0					,torch_dtype=torch.floataa     )
      pipe("""This is a test"""     )





   @require_torch
   @require_accelerate
   @require_torch_gpu
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      import torch

      __SCREAMING_SNAKE_CASE   				=							pipeline(model="""hf-internal-testing/tiny-random-bloom"""					,device_map="""auto"""					,torch_dtype=torch.floataa     )
      pipe("""This is a test"""					,do_sample=lowerCamelCase					,top_p=0.5     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""Hello world"""
      __SCREAMING_SNAKE_CASE   				=							pipeline("""text-generation"""					,model="""hf-internal-testing/tiny-random-gpt2"""     )
      if text_generator.model.framework == "tf":
         __SCREAMING_SNAKE_CASE   				=							logging.get_logger("""transformers.generation.tf_utils"""     )
      else:
         __SCREAMING_SNAKE_CASE   				=							logging.get_logger("""transformers.generation.utils"""     )
      __SCREAMING_SNAKE_CASE   				=							"""Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

      # Both are set by the user -> log warning
      with CaptureLogger(lowerCamelCase     ) as cl:
         __SCREAMING_SNAKE_CASE   				=							text_generator(lowerCamelCase					,max_length=10					,max_new_tokens=1     )
      self.assertIn(lowerCamelCase					,cl.out     )

      # The user only sets one -> no warning
      with CaptureLogger(lowerCamelCase     ) as cl:
         __SCREAMING_SNAKE_CASE   				=							text_generator(lowerCamelCase					,max_new_tokens=1     )
      self.assertNotIn(lowerCamelCase					,cl.out     )

      with CaptureLogger(lowerCamelCase     ) as cl:
         __SCREAMING_SNAKE_CASE   				=							text_generator(lowerCamelCase					,max_length=10     )
      self.assertNotIn(lowerCamelCase					,cl.out     )












'''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()





