





'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    a   	=    "platform"

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						)    ->       List[str]:

   '''simple docstring'''







   if attention_mask is None:
      __SCREAMING_SNAKE_CASE   				=							np.where(input_ids != config.pad_token_id				,						1				,						0   )
   if decoder_attention_mask is None:
      __SCREAMING_SNAKE_CASE   				=							np.where(decoder_input_ids != config.pad_token_id				,						1				,						0   )
   if head_mask is None:
      __SCREAMING_SNAKE_CASE   				=							np.ones((config.encoder_layers, config.encoder_attention_heads)   )
   if decoder_head_mask is None:
      __SCREAMING_SNAKE_CASE   				=							np.ones((config.decoder_layers, config.decoder_attention_heads)   )
   if cross_attn_head_mask is None:
      __SCREAMING_SNAKE_CASE   				=							np.ones((config.decoder_layers, config.decoder_attention_heads)   )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }







class __a			:





   def __init__(       self					:      Dict					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict=13					,lowerCamelCase					:      Any=7					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      Union[str, Any]=False					,lowerCamelCase					:      str=99					,lowerCamelCase					:      int=16					,lowerCamelCase					:      List[str]=2					,lowerCamelCase					:      Union[str, Any]=4					,lowerCamelCase					:      Dict=4					,lowerCamelCase					:      Optional[Any]="gelu"					,lowerCamelCase					:      Dict=0.1					,lowerCamelCase					:      List[Any]=0.1					,lowerCamelCase					:      Any=32					,lowerCamelCase					:      Optional[Any]=2					,lowerCamelCase					:      int=1					,lowerCamelCase					:      int=0					,lowerCamelCase					:      Optional[int]=0.02					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							seq_length
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							eos_token_id
      __SCREAMING_SNAKE_CASE   				=							pad_token_id
      __SCREAMING_SNAKE_CASE   				=							bos_token_id
      __SCREAMING_SNAKE_CASE   				=							initializer_range





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,self.vocab_size     )					,3					,self.vocab_size     )
      __SCREAMING_SNAKE_CASE   				=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,dtype=np.intaa     ))					,-1     )

      __SCREAMING_SNAKE_CASE   				=							shift_tokens_right(UpperCamelCase_					,1					,2     )

      __SCREAMING_SNAKE_CASE   				=							BlenderbotConfig(
          vocab_size=self.vocab_size					,d_model=self.hidden_size					,encoder_layers=self.num_hidden_layers					,decoder_layers=self.num_hidden_layers					,encoder_attention_heads=self.num_attention_heads					,decoder_attention_heads=self.num_attention_heads					,encoder_ffn_dim=self.intermediate_size					,decoder_ffn_dim=self.intermediate_size					,dropout=self.hidden_dropout_prob					,attention_dropout=self.attention_probs_dropout_prob					,max_position_embeddings=self.max_position_embeddings					,eos_token_id=self.eos_token_id					,bos_token_id=self.bos_token_id					,pad_token_id=self.pad_token_id					,initializer_range=self.initializer_range					,use_cache=UpperCamelCase_					,)
      __SCREAMING_SNAKE_CASE   				=							prepare_blenderbot_inputs_dict(UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_     )
      return config, inputs_dict





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      return config, inputs_dict





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							20
      __SCREAMING_SNAKE_CASE   				=							model_class_name(UpperCamelCase_     )

      __SCREAMING_SNAKE_CASE   				=							model.encode(inputs_dict["""input_ids"""]     )

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							(
          inputs_dict["""decoder_input_ids"""],
          inputs_dict["""decoder_attention_mask"""],
      )

      __SCREAMING_SNAKE_CASE   				=							model.init_cache(decoder_input_ids.shape[0]					,UpperCamelCase_					,UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,dtype="""i4"""     )

      __SCREAMING_SNAKE_CASE   				=							jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,)
      __SCREAMING_SNAKE_CASE   				=							model.decode(
          decoder_input_ids[:, :-1]					,UpperCamelCase_					,decoder_attention_mask=UpperCamelCase_					,past_key_values=UpperCamelCase_					,decoder_position_ids=UpperCamelCase_					,)

      __SCREAMING_SNAKE_CASE   				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,dtype="""i4"""     )
      __SCREAMING_SNAKE_CASE   				=							model.decode(
          decoder_input_ids[:, -1:]					,UpperCamelCase_					,decoder_attention_mask=UpperCamelCase_					,past_key_values=outputs_cache.past_key_values					,decoder_position_ids=UpperCamelCase_					,)

      __SCREAMING_SNAKE_CASE   				=							model.decode(UpperCamelCase_					,UpperCamelCase_     )

      __SCREAMING_SNAKE_CASE   				=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
      self.parent.assertTrue(diff < 1E-3					,msg=f"""Max diff is {diff}"""     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							20
      __SCREAMING_SNAKE_CASE   				=							model_class_name(UpperCamelCase_     )

      __SCREAMING_SNAKE_CASE   				=							model.encode(inputs_dict["""input_ids"""]     )

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							(
          inputs_dict["""decoder_input_ids"""],
          inputs_dict["""decoder_attention_mask"""],
      )

      __SCREAMING_SNAKE_CASE   				=							jnp.concatenate(
          [
              decoder_attention_mask,
              jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
          ]					,axis=-1					,)

      __SCREAMING_SNAKE_CASE   				=							model.init_cache(decoder_input_ids.shape[0]					,UpperCamelCase_					,UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,)

      __SCREAMING_SNAKE_CASE   				=							model.decode(
          decoder_input_ids[:, :-1]					,UpperCamelCase_					,decoder_attention_mask=UpperCamelCase_					,past_key_values=UpperCamelCase_					,decoder_position_ids=UpperCamelCase_					,)
      __SCREAMING_SNAKE_CASE   				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,dtype="""i4"""     )
      __SCREAMING_SNAKE_CASE   				=							model.decode(
          decoder_input_ids[:, -1:]					,UpperCamelCase_					,past_key_values=outputs_cache.past_key_values					,decoder_attention_mask=UpperCamelCase_					,decoder_position_ids=UpperCamelCase_					,)

      __SCREAMING_SNAKE_CASE   				=							model.decode(UpperCamelCase_					,UpperCamelCase_					,decoder_attention_mask=UpperCamelCase_     )

      __SCREAMING_SNAKE_CASE   				=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
      self.parent.assertTrue(diff < 1E-3					,msg=f"""Max diff is {diff}"""     )







@require_flax
class __a			( unittest.TestCase      ):
   __UpperCamelCase :			Dict						   =						99





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.array(
          [
              [71, 82, 18, 33, 46, 91, 2],
              [68, 34, 26, 58, 30, 82, 2],
              [5, 97, 17, 39, 94, 40, 2],
              [76, 83, 94, 25, 70, 78, 2],
              [87, 59, 41, 35, 48, 66, 2],
              [55, 13, 16, 58, 5, 2, 1],  # note padding
              [64, 27, 31, 51, 12, 75, 2],
              [52, 64, 86, 17, 83, 39, 2],
              [48, 61, 9, 24, 71, 82, 2],
              [26, 1, 60, 48, 22, 13, 2],
              [21, 5, 62, 28, 14, 76, 2],
              [45, 98, 37, 86, 59, 48, 2],
              [70, 70, 50, 9, 28, 0, 2],
          ]					,dtype=np.intaa					,)

      __SCREAMING_SNAKE_CASE   				=							input_ids.shape[0]
      __SCREAMING_SNAKE_CASE   				=							BlenderbotConfig(
          vocab_size=self.vocab_size					,d_model=24					,encoder_layers=2					,decoder_layers=2					,encoder_attention_heads=2					,decoder_attention_heads=2					,encoder_ffn_dim=32					,decoder_ffn_dim=32					,max_position_embeddings=48					,eos_token_id=2					,pad_token_id=1					,bos_token_id=0					,)
      return config, input_ids, batch_size





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self._get_config_and_data()
      __SCREAMING_SNAKE_CASE   				=							FlaxBlenderbotForConditionalGeneration(UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							lm_model(input_ids=UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							(batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs["""logits"""].shape					,UpperCamelCase_     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							BlenderbotConfig(
          vocab_size=self.vocab_size					,d_model=14					,encoder_layers=2					,decoder_layers=2					,encoder_attention_heads=2					,decoder_attention_heads=2					,encoder_ffn_dim=8					,decoder_ffn_dim=8					,max_position_embeddings=48					,)
      __SCREAMING_SNAKE_CASE   				=							FlaxBlenderbotForConditionalGeneration(UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,dtype=np.intaa     )
      __SCREAMING_SNAKE_CASE   				=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,dtype=np.intaa     )
      __SCREAMING_SNAKE_CASE   				=							lm_model(input_ids=UpperCamelCase_					,decoder_input_ids=UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							(*summary.shape, config.vocab_size)
      self.assertEqual(outputs["""logits"""].shape					,UpperCamelCase_     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,dtype=np.intaa     )
      __SCREAMING_SNAKE_CASE   				=							shift_tokens_right(UpperCamelCase_					,1					,2     )
      __SCREAMING_SNAKE_CASE   				=							np.equal(UpperCamelCase_					,1     ).astype(np.floataa     ).sum()
      __SCREAMING_SNAKE_CASE   				=							np.equal(UpperCamelCase_					,1     ).astype(np.floataa     ).sum()
      self.assertEqual(shifted.shape					,input_ids.shape     )
      self.assertEqual(UpperCamelCase_					,n_pad_before - 1     )
      self.assertTrue(np.equal(shifted[:, 0]					,2     ).all()     )







@require_flax
class __a			( lowercase_, unittest.TestCase, lowercase_      ):
   __UpperCamelCase :			Optional[Any]						   =						True
   __UpperCamelCase :			List[str]						   =						(
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   __UpperCamelCase :			Any						   =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							FlaxBlenderbotModelTester(self     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_     )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__     ):
            __SCREAMING_SNAKE_CASE   				=							self._prepare_for_class(UpperCamelCase_					,UpperCamelCase_     )
            __SCREAMING_SNAKE_CASE   				=							model_class(UpperCamelCase_     )

            @jax.jit
            def encode_jitted(lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]=None					,**lowerCamelCase					:      List[str]     ):
               return model.encode(input_ids=UpperCamelCase_					,attention_mask=UpperCamelCase_     )

            with self.subTest("""JIT Enabled"""     ):
               __SCREAMING_SNAKE_CASE   				=							encode_jitted(**UpperCamelCase_     ).to_tuple()

            with self.subTest("""JIT Disabled"""     ):
               with jax.disable_jit():
                  __SCREAMING_SNAKE_CASE   				=							encode_jitted(**UpperCamelCase_     ).to_tuple()

            self.assertEqual(len(UpperCamelCase_     )					,len(UpperCamelCase_     )     )
            for jitted_output, output in zip(UpperCamelCase_					,UpperCamelCase_     ):
               self.assertEqual(jitted_output.shape					,output.shape     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__     ):
            __SCREAMING_SNAKE_CASE   				=							model_class(UpperCamelCase_     )
            __SCREAMING_SNAKE_CASE   				=							model.encode(inputs_dict["""input_ids"""]					,inputs_dict["""attention_mask"""]     )

            __SCREAMING_SNAKE_CASE   				=							{
                """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                """encoder_outputs""": encoder_outputs,
            }

            @jax.jit
            def decode_jitted(lowerCamelCase					:      List[str]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      int     ):
               return model.decode(
                   decoder_input_ids=UpperCamelCase_					,decoder_attention_mask=UpperCamelCase_					,encoder_outputs=UpperCamelCase_					,)

            with self.subTest("""JIT Enabled"""     ):
               __SCREAMING_SNAKE_CASE   				=							decode_jitted(**UpperCamelCase_     ).to_tuple()

            with self.subTest("""JIT Disabled"""     ):
               with jax.disable_jit():
                  __SCREAMING_SNAKE_CASE   				=							decode_jitted(**UpperCamelCase_     ).to_tuple()

            self.assertEqual(len(UpperCamelCase_     )					,len(UpperCamelCase_     )     )
            for jitted_output, output in zip(UpperCamelCase_					,UpperCamelCase_     ):
               self.assertEqual(jitted_output.shape					,output.shape     )





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

      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""     )
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         __SCREAMING_SNAKE_CASE   				=							np.ones((1, 1)     ) * model.config.eos_token_id
         __SCREAMING_SNAKE_CASE   				=							model(UpperCamelCase_     )
         self.assertIsNotNone(UpperCamelCase_     )





   @unittest.skipUnless(jax_device != """cpu"""					,"""3B test too slow on CPU."""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
      __SCREAMING_SNAKE_CASE   				=							{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

      __SCREAMING_SNAKE_CASE   				=							FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""					,from_pt=UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )

      __SCREAMING_SNAKE_CASE   				=							["""Sam"""]
      __SCREAMING_SNAKE_CASE   				=							tokenizer(UpperCamelCase_					,return_tensors="""jax"""     )

      __SCREAMING_SNAKE_CASE   				=							model.generate(**UpperCamelCase_					,**UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							"""Sam is a great name. It means \"sun\" in Gaelic."""

      __SCREAMING_SNAKE_CASE   				=							tokenizer.batch_decode(UpperCamelCase_					,**UpperCamelCase_     )
      assert generated_txt[0].strip() == tgt_text













from __future__ import annotations

from typing import Any


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      Dict			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      float = 0					):


         """simple docstring"""



         __A     ,						__A       =						row, column
         __A       =						[[default_value for c in range(UpperCamelCase_					)] for r in range(UpperCamelCase_					)]




     def __str__(					self							:      Any					):


         """simple docstring"""



         __A       =						F"Matrix consist of {self.row} rows and {self.column} columns\n"

         # Make string identifier
         __A       =						0
         for row_vector in self.array:
             for obj in row_vector:
                 __A       =						max(UpperCamelCase_			,						len(str(UpperCamelCase_					)					)					)
         __A       =						F"%{max_element_length}s"

         # Make string and return
         def single_line(UpperCamelCase_							:      list[float]					) -> str:
             nonlocal string_format_identifier
             __A       =						"""["""
             line += ", ".join(string_format_identifier % (obj,) for obj in row_vector					)
             line += "]"
             return line

         s += "\n".join(single_line(UpperCamelCase_					) for row_vector in self.array					)
         return s




     def __repr__(					self							:      Optional[Any]					):


         """simple docstring"""



         return str(self					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         if not (isinstance(UpperCamelCase_			,						(list, tuple)					) and len(UpperCamelCase_					) == 2):
             return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
         else:
             return True




     def __getitem__(					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         return self.array[loc[0]][loc[1]]




     def __setitem__(					self							:      str			,						UpperCamelCase_							:      tuple[int, int]			,						UpperCamelCase_							:      float					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         __A       =						value




     def __add__(					self							:      Optional[int]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == another.row and self.column == another.column

         # Add
         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c] + another[r, c]
         return result




     def __neg__(					self							:      Any					):


         """simple docstring"""



         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						-self[r, c]
         return result




     def __sub__(					self							:      List[Any]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         return self + (-another)




     def __mul__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      int | float | Matrix					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						(int, float)					):  # Scalar multiplication
             __A       =						Matrix(self.row			,						self.column					)
             for r in range(self.row					):
                 for c in range(self.column					):
                     __A       =						self[r, c] * another
             return result
         elif isinstance(UpperCamelCase_			,						UpperCamelCase_					):  # Matrix multiplication
             assert self.column == another.row
             __A       =						Matrix(self.row			,						another.column					)
             for r in range(self.row					):
                 for c in range(another.column					):
                     for i in range(self.column					):
                         result[r, c] += self[r, i] * another[i, c]
             return result
         else:
             __A       =						F"Unsupported type given for another ({type(UpperCamelCase_					)})"
             raise TypeError(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						Matrix(self.column			,						self.row					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c]
         return result





     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Matrix			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					) and isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         __A       =						v.transpose()
         __A       =						(v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
             return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      __A       =						Matrix(3      ,					3      ,					0     )
      for i in range(3     ):
          __A       =						1
      print(f"a^(-1) is {ainv}"     )
      # u, v
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						1, 2, -3
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						4, -2, 5
      print(f"u is {u}"     )
      print(f"v is {v}"     )
      print(f"uv^T is {u * v.transpose()}"     )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase      ,					__lowercase     )}"     )



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      import doctest

      doctest.testmod()






  testa()




