


import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      import torch

      from transformers import GPTaLMHeadModel




@require_torch
@require_sentencepiece
@require_tokenizers
class  A_ (			unittest.TestCase  ):







 """simple docstring"""





 @slow
 @unittest.skip("Model is not available."					)
 def 	_UpperCamelCase     (      self       :			Union[str, Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					str			=      "nvidia/megatron-gpt2-345m/"
      if "MYDIR" in os.environ:
           UpperCAmelCase    :					Tuple			=      os.path.join(os.environ["MYDIR"]							,     _a					)
      UpperCAmelCase    :					Dict			=      GPTaLMHeadModel.from_pretrained(_a					)
      model.to(_a					)
      model.half()

      UpperCAmelCase    :					str			=      torch.tensor(
          [[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]							,     device=_a							,     dtype=torch.long							,     )

      with torch.no_grad():
           UpperCAmelCase    :					List[str]			=      model(_a					).logits

      UpperCAmelCase    :					Tuple			=      torch.Size((1, 9, 5_0_2_5_7)					)
      self.assertEqual(output.shape							,     _a					)

      UpperCAmelCase    :					Any			=      torch.tensor(
          [
              4.9_4_1_4,
              -0.2_9_2_0,
              -1.2_1_4_8,
              -4.0_2_7_3,
              -0.5_1_6_1,
              -5.2_1_0_9,
              -1.2_4_1_2,
              -1.8_3_0_1,
              -1.7_7_3_4,
              -4.7_1_4_8,
              -0.2_3_1_7,
              -1.0_8_1_1,
              -2.1_7_7_7,
              0.4_1_4_1,
              -3.7_9_6_9,
              -4.0_5_8_6,
              -2.5_3_3_2,
              -3.3_8_0_9,
              4.3_8_6_7,
          ]							,     device=_a							,     dtype=torch.half							,     )

      for i in range(1_9					):
           UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
           UpperCAmelCase	,			UpperCAmelCase    :					int			=      output[0, r, c], expected_diag[i]
           UpperCAmelCase    :					str			=      f'row={r} col={c} computed={computed} expected={expected}'
           self.assertAlmostEqual(_a							,     _a							,     delta=1E-4							,     msg=_a					)







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case				=			[[1_2, 1_0], [3, 9]]
_snake_case				=			[[3, 4], [7, 4]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case				=			[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case				=			[1]
_snake_case				=			[2]

_snake_case				=			logging.getLogger()
_snake_case				=			logging.DEBUG
_snake_case				=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} returned integer'			)
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_addition.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) + np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[Any]			=      matop.add(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} with different matrix dims'			)
               matop.add(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} returned integer'			)
               matop.subtract(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) - np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[int]			=      matop.subtract(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          logger.info(f'\n\t{test_multiplication.__name__} returned integer'			)
          with pytest.raises(A__			):
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'			)
          UpperCAmelCase    :					List[str]			=      (np.matmul(A__		,		A__			)).tolist()
          UpperCAmelCase    :					int			=      matop.multiply(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(
                   f'\n\t{test_multiplication.__name__} does not meet dim requirements'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					int			=      (3.5 * np.array(A__			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.scalar_multiply(A__		,		3.5			)
     assert theo == act







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					List[Any]			=      (np.identity(5			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.identity(5			)
     assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"		,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]			)
def 	A (		A__			):
     if (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_transpose.__name__} returned integer'			)
               matop.transpose(A__			)
     else:
          UpperCAmelCase    :					Dict			=      (np.transpose(A__			)).tolist()
          UpperCAmelCase    :					List[str]			=      matop.transpose(A__		,		return_map=A__			)
          assert theo == act



