




'''simple docstring'''




import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

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

UpperCAmelCase_       				=      logging.getLogger()
UpperCAmelCase_       				=      logging.DEBUG
UpperCAmelCase_       				=      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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,       SCREAMING_SNAKE_CASE__			:      List[Any]       ):
 '''simple docstring'''





 if (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2) or (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2):
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   logger.info(F'''\n\t{test_addition.__name__} returned integer'''       )
   matop.add(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )
 elif (np.array(SCREAMING_SNAKE_CASE__       )).shape == (np.array(SCREAMING_SNAKE_CASE__       )).shape:
  logger.info(F'''\n\t{test_addition.__name__} with same matrix dims'''       )
  UpperCAmelCase__										=    (np.array(SCREAMING_SNAKE_CASE__       ) + np.array(SCREAMING_SNAKE_CASE__       )).tolist()
  UpperCAmelCase__										=    matop.add(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )
  assert theo == act
 else:
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   logger.info(F'''\n\t{test_addition.__name__} with different matrix dims'''       )
   matop.add(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )

@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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,       SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 if (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2) or (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2):
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   logger.info(F'''\n\t{test_subtraction.__name__} returned integer'''       )
   matop.subtract(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )
 elif (np.array(SCREAMING_SNAKE_CASE__       )).shape == (np.array(SCREAMING_SNAKE_CASE__       )).shape:
  logger.info(F'''\n\t{test_subtraction.__name__} with same matrix dims'''       )
  UpperCAmelCase__										=    (np.array(SCREAMING_SNAKE_CASE__       ) - np.array(SCREAMING_SNAKE_CASE__       )).tolist()
  UpperCAmelCase__										=    matop.subtract(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )
  assert theo == act
 else:
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   logger.info(F'''\n\t{test_subtraction.__name__} with different matrix dims'''       )
   assert matop.subtract(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )

@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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[Any]       ,       SCREAMING_SNAKE_CASE__			:      Optional[Any]       ):
 '''simple docstring'''





 if (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2) or (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2):
  logger.info(F'''\n\t{test_multiplication.__name__} returned integer'''       )
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   matop.add(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )
 elif (np.array(SCREAMING_SNAKE_CASE__       )).shape == (np.array(SCREAMING_SNAKE_CASE__       )).shape:
  logger.info(F'''\n\t{test_multiplication.__name__} meets dim requirements'''       )
  UpperCAmelCase__										=    (np.matmul(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )).tolist()
  UpperCAmelCase__										=    matop.multiply(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )
  assert theo == act
 else:
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   logger.info(
       F'''\n\t{test_multiplication.__name__} does not meet dim requirements'''       )
   assert matop.subtract(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__       )

@pytest.mark.mat_ops
def    _UpperCamelCase      (				):
 '''simple docstring'''





 UpperCAmelCase__										=    (3.5 * np.array(SCREAMING_SNAKE_CASE__       )).tolist()
 UpperCAmelCase__										=    matop.scalar_multiply(SCREAMING_SNAKE_CASE__       ,       3.5       )
 assert theo == act

@pytest.mark.mat_ops
def    _UpperCamelCase      (				):
 '''simple docstring'''





 UpperCAmelCase__										=    (np.identity(5       )).tolist()
 UpperCAmelCase__										=    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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 if (np.array(SCREAMING_SNAKE_CASE__       )).shape < (2, 2):
  with pytest.raises(SCREAMING_SNAKE_CASE__       ):
   logger.info(F'''\n\t{test_transpose.__name__} returned integer'''       )
   matop.transpose(SCREAMING_SNAKE_CASE__       )
 else:
  UpperCAmelCase__										=    (np.transpose(SCREAMING_SNAKE_CASE__       )).tolist()
  UpperCAmelCase__										=    matop.transpose(SCREAMING_SNAKE_CASE__       ,       return_map=SCREAMING_SNAKE_CASE__       )
  assert theo == act






'''simple docstring'''




import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
 model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    UpperCAmelCase_       				=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
