


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
   import torch

   from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=13					,   A_=7					,   A_=True					,   A_=True					,   A_=False					,   A_=True					,   A_=99					,   A_=32					,   A_=5					,   A_=4					,   A_=37					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=16					,   A_=2					,   A_=0.02					,   A_=3					,   A_=4					,   A_=None					,   )  ->       int:
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=seq_length
         __UpperCamelCase    							=is_training
         __UpperCamelCase    							=use_input_mask
         __UpperCamelCase    							=use_token_type_ids
         __UpperCamelCase    							=use_labels
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=type_vocab_size
         __UpperCamelCase    							=type_sequence_label_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=num_labels
         __UpperCamelCase    							=num_choices
         __UpperCamelCase    							=scope






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )

         __UpperCamelCase    							=None
         if self.use_input_mask:
              __UpperCamelCase    							=random_attention_mask([self.batch_size, self.seq_length]    )

         __UpperCamelCase    							=None
         if self.use_token_type_ids:
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size    )

         __UpperCamelCase    							=None
         __UpperCamelCase    							=None
         __UpperCamelCase    							=None
         if self.use_labels:
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.type_sequence_label_size    )
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels    )
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.num_choices    )

         __UpperCamelCase    							=self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def _a					(			self    )  ->       int:
         return OpenLlamaConfig(
             vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=A_					,   initializer_range=self.initializer_range					,   use_stable_embedding=A_					,   )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Dict:
         __UpperCamelCase    							=OpenLlamaModel(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_    )
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       Optional[int]:
         __UpperCamelCase    							=True
         __UpperCamelCase    							=OpenLlamaModel(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   encoder_hidden_states=A_					,   encoder_attention_mask=A_					,   )
         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   encoder_hidden_states=A_					,   )
         __UpperCamelCase    							=model(A_					,   attention_mask=A_    )
         self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=OpenLlamaForCausalLM(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   labels=A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       List[Any]:
         __UpperCamelCase    							=True
         __UpperCamelCase    							=True
         __UpperCamelCase    							=OpenLlamaForCausalLM(config=A_    )
         model.to(A_    )
         model.eval()

         # first forward pass
         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   encoder_hidden_states=A_					,   encoder_attention_mask=A_					,   use_cache=A_					,   )
         __UpperCamelCase    							=outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         __UpperCamelCase    							=ids_tensor((self.batch_size, 3)					,   config.vocab_size    )
         __UpperCamelCase    							=ids_tensor((self.batch_size, 3)					,   vocab_size=2    )

         # append to next input_ids and
         __UpperCamelCase    							=torch.cat([input_ids, next_tokens]					,   dim=-1    )
         __UpperCamelCase    							=torch.cat([input_mask, next_mask]					,   dim=-1    )

         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   encoder_hidden_states=A_					,   encoder_attention_mask=A_					,   output_hidden_states=A_					,   )['hidden_states'][0]
         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   encoder_hidden_states=A_					,   encoder_attention_mask=A_					,   past_key_values=A_					,   output_hidden_states=A_					,   )['hidden_states'][0]

         # select random slice
         __UpperCamelCase    							=ids_tensor((1,)					,   output_from_past.shape[-1]    ).item()
         __UpperCamelCase    							=output_from_no_past[:, -3:, random_slice_idx].detach()
         __UpperCamelCase    							=output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

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






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      
         )    							=config_and_inputs
         __UpperCamelCase    							={'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class     UpperCAmelCase__       (					A_   ,  A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  (
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    UpperCAmelCase__						:		List[Any]      =  (OpenLlamaForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase__						:		str      =  (
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__						:		Optional[Any]      =  False
    UpperCAmelCase__						:		Optional[int]      =  False






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=OpenLlamaModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   hidden_size=37    )






    def _a					(			self    )  ->       Tuple:
         self.config_tester.run_common_tests()






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              __UpperCamelCase    							=type
              self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							=input_dict['input_ids']
         __UpperCamelCase    							=input_ids.ne(1    ).to(A_    )
         __UpperCamelCase    							=ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size    )
         __UpperCamelCase    							=OpenLlamaForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   labels=A_    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							='single_label_classification'
         __UpperCamelCase    							=input_dict['input_ids']
         __UpperCamelCase    							=input_ids.ne(1    ).to(A_    )
         __UpperCamelCase    							=ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size    )
         __UpperCamelCase    							=OpenLlamaForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   labels=A_    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							='multi_label_classification'
         __UpperCamelCase    							=input_dict['input_ids']
         __UpperCamelCase    							=input_ids.ne(1    ).to(A_    )
         __UpperCamelCase    							=ids_tensor(
             [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size    ).to(torch.float    )
         __UpperCamelCase    							=OpenLlamaForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   labels=A_    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'    )
    def _a					(			self    )  ->       List[Any]:
         pass






    @parameterized.expand([('linear',), ('dynamic',)]    )
    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=ids_tensor([1, 10]					,   config.vocab_size    )
         __UpperCamelCase    							=ids_tensor([1, int(config.max_position_embeddings * 1.5    )]					,   config.vocab_size    )

         set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
         __UpperCamelCase    							=OpenLlamaModel(A_    )
         original_model.to(A_    )
         original_model.eval()
         __UpperCamelCase    							=original_model(A_    ).last_hidden_state
         __UpperCamelCase    							=original_model(A_    ).last_hidden_state

         set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
         __UpperCamelCase    							={'type': scaling_type, 'factor': 10.0}
         __UpperCamelCase    							=OpenLlamaModel(A_    )
         scaled_model.to(A_    )
         scaled_model.eval()
         __UpperCamelCase    							=scaled_model(A_    ).last_hidden_state
         __UpperCamelCase    							=scaled_model(A_    ).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
              self.assertTrue(torch.allclose(A_					,   A_					,   atol=1E-5    )    )
         else:
              self.assertFalse(torch.allclose(A_					,   A_					,   atol=1E-5    )    )

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(A_					,   A_					,   atol=1E-5    )    )









from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_ = None    )  ->       None:
         if components is None:
              __UpperCamelCase    							=[]
         __UpperCamelCase    							=list(A_    )






    def __len__(			self    )  ->       int:
         return len(self.__components    )






    def __str__(			self    )  ->       str:
         return "(" + ",".join(map(A_					,   self.__components    )    ) + ")"






    def __add__(			self					,   A_    )  ->       Vector:
         __UpperCamelCase    							=len(self    )
         if size == len(A_    ):
              __UpperCamelCase    							=[self.__components[i] + other.component(A_    ) for i in range(A_    )]
              return Vector(A_    )
         else:
              raise Exception('must have the same size'    )






    def __sub__(			self					,   A_    )  ->       Vector:
         __UpperCamelCase    							=len(self    )
         if size == len(A_    ):
              __UpperCamelCase    							=[self.__components[i] - other.component(A_    ) for i in range(A_    )]
              return Vector(A_    )
         else:  # error case
              raise Exception('must have the same size'    )






    @overload
    def __mul__(			self					,   A_    )  ->       Vector:
         ...






    @overload
    def __mul__(			self					,   A_    )  ->       float:
         ...






    def __mul__(			self					,   A_    )  ->       float | Vector:
         if isinstance(A_					,   (float, int)    ):
              __UpperCamelCase    							=[c * other for c in self.__components]
              return Vector(A_    )
         elif isinstance(A_					,   A_    ) and len(self    ) == len(A_    ):
              __UpperCamelCase    							=len(self    )
              __UpperCamelCase    							=[self.__components[i] * other.component(A_    ) for i in range(A_    )]
              return sum(A_    )
         else:  # error case
              raise Exception('invalid operand!'    )






    def _a					(			self    )  ->       Vector:
         return Vector(self.__components    )






    def _a					(			self					,   A_    )  ->       float:
         if isinstance(A_					,   A_    ) and -len(self.__components    ) <= i < len(self.__components    ):
              return self.__components[i]
         else:
              raise Exception('index out of range'    )






    def _a					(			self					,   A_					,   A_    )  ->       None:
         assert -len(self.__components    ) <= pos < len(self.__components    )
         __UpperCamelCase    							=value






    def _a					(			self    )  ->       float:
         if len(self.__components    ) == 0:
              raise Exception('Vector is empty'    )
         __UpperCamelCase    							=[c**2 for c in self.__components]
         return math.sqrt(sum(A_    )    )






    def _a					(			self					,   A_					,   A_ = False    )  ->       float:
         __UpperCamelCase    							=self * other
         __UpperCamelCase    							=self.euclidean_length() * other.euclidean_length()
         if deg:
              return math.degrees(math.acos(num / den    )    )
         else:
              return math.acos(num / den    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     assert isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     return Vector([0] * dimension				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     assert isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) and (isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				))
     __UpperCamelCase    							=[0] * dimension
     __UpperCamelCase    							=1
     return Vector(SCREAMING_SNAKE_CASE__				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					float			,     SCREAMING_SNAKE_CASE__			:					Vector			,     SCREAMING_SNAKE_CASE__			:					Vector				):
     assert (
         isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
         and isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
         and (isinstance(SCREAMING_SNAKE_CASE__			,     (int, float)				))
     )
     return x * scalar + y

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     random.seed(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[random.randint(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)]
     return Vector(SCREAMING_SNAKE_CASE__				)






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_    )  ->       None:
         __UpperCamelCase    							=matrix
         __UpperCamelCase    							=w
         __UpperCamelCase    							=h






    def __str__(			self    )  ->       str:
         __UpperCamelCase    							=''
         for i in range(self.__height    ):
              ans += "|"
              for j in range(self.__width    ):
                   if j < self.__width - 1:
                        ans += str(self.__matrix[i][j]    ) + ","
                   else:
                        ans += str(self.__matrix[i][j]    ) + "|\n"
         return ans






    def __add__(			self					,   A_    )  ->       Matrix:
         if self.__width == other.width() and self.__height == other.height():
              __UpperCamelCase    							=[]
              for i in range(self.__height    ):
                   __UpperCamelCase    							=[
                       self.__matrix[i][j] + other.component(A_					,   A_    )
                       for j in range(self.__width    )
                   ]
                   matrix.append(A_    )
              return Matrix(A_					,   self.__width					,   self.__height    )
         else:
              raise Exception('matrix must have the same dimension!'    )






    def __sub__(			self					,   A_    )  ->       Matrix:
         if self.__width == other.width() and self.__height == other.height():
              __UpperCamelCase    							=[]
              for i in range(self.__height    ):
                   __UpperCamelCase    							=[
                       self.__matrix[i][j] - other.component(A_					,   A_    )
                       for j in range(self.__width    )
                   ]
                   matrix.append(A_    )
              return Matrix(A_					,   self.__width					,   self.__height    )
         else:
              raise Exception('matrices must have the same dimension!'    )






    @overload
    def __mul__(			self					,   A_    )  ->       Matrix:
         ...






    @overload
    def __mul__(			self					,   A_    )  ->       Vector:
         ...






    def __mul__(			self					,   A_    )  ->       Vector | Matrix:
         if isinstance(A_					,   A_    ):  # matrix-vector
              if len(A_    ) == self.__width:
                   __UpperCamelCase    							=zero_vector(self.__height    )
                   for i in range(self.__height    ):
                        __UpperCamelCase    							=[
                            self.__matrix[i][j] * other.component(A_    )
                            for j in range(self.__width    )
                        ]
                        ans.change_component(A_					,   sum(A_    )    )
                   return ans
              else:
                   raise Exception(
                       'vector must have the same size as the '
                       'number of columns of the matrix!'    )
         elif isinstance(A_					,   (int, float)    ):  # matrix-scalar
              __UpperCamelCase    							=[
                  [self.__matrix[i][j] * other for j in range(self.__width    )]
                  for i in range(self.__height    )
              ]
              return Matrix(A_					,   self.__width					,   self.__height    )
         return None






    def _a					(			self    )  ->       int:
         return self.__height






    def _a					(			self    )  ->       int:
         return self.__width






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if 0 <= x < self.__height and 0 <= y < self.__width:
              return self.__matrix[x][y]
         else:
              raise Exception('change_component: indices out of bounds'    )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       None:
         if 0 <= x < self.__height and 0 <= y < self.__width:
              __UpperCamelCase    							=value
         else:
              raise Exception('change_component: indices out of bounds'    )






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         __UpperCamelCase    							=self.__matrix[:x] + self.__matrix[x + 1 :]
         for i in range(len(A_    )    ):
              __UpperCamelCase    							=minor[i][:y] + minor[i][y + 1 :]
         return Matrix(A_					,   self.__width - 1					,   self.__height - 1    ).determinant()






    def _a					(			self					,   A_					,   A_    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         if 0 <= x < self.__height and 0 <= y < self.__width:
              return (-1) ** (x + y) * self.minor(A_					,   A_    )
         else:
              raise Exception('Indices out of bounds'    )






    def _a					(			self    )  ->       float:
         if self.__height != self.__width:
              raise Exception('Matrix is not square'    )
         if self.__height < 1:
              raise Exception('Matrix has no element'    )
         elif self.__height == 1:
              return self.__matrix[0][0]
         elif self.__height == 2:
              return (
                  self.__matrix[0][0] * self.__matrix[1][1]
                  - self.__matrix[0][1] * self.__matrix[1][0]
              )
         else:
              __UpperCamelCase    							=[
                  self.__matrix[0][y] * self.cofactor(0					,   A_    ) for y in range(self.__width    )
              ]
              return sum(A_    )

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=[[0] * n for _ in range(SCREAMING_SNAKE_CASE__				)]
     return Matrix(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     random.seed(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[
         [random.randint(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for _ in range(SCREAMING_SNAKE_CASE__				)] for _ in range(SCREAMING_SNAKE_CASE__				)
     ]
     return Matrix(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)





