



"""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        __magic_name__			(							lowercase							,     lowercase	):
   if (np.array(lowercase	)).shape < (2, 2) or (np.array(lowercase	)).shape < (2, 2):
      with pytest.raises(lowercase	):
         logger.info(f'''\n\t{test_addition.__name__} returned integer'''	)
         matop.add(lowercase							,     lowercase	)
   elif (np.array(lowercase	)).shape == (np.array(lowercase	)).shape:
      logger.info(f'''\n\t{test_addition.__name__} with same matrix dims'''	)
      SCREAMING_SNAKE_CASE_:	str            =(np.array(lowercase	) + np.array(lowercase	)).tolist()
      SCREAMING_SNAKE_CASE_:	List[Any]            =matop.add(lowercase							,     lowercase	)
      assert theo == act
   else:
      with pytest.raises(lowercase	):
         logger.info(f'''\n\t{test_addition.__name__} with different matrix dims'''	)
         matop.add(lowercase							,     lowercase	)





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





@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        __magic_name__			(							lowercase							,     lowercase	):
   if (np.array(lowercase	)).shape < (2, 2) or (np.array(lowercase	)).shape < (2, 2):
      logger.info(f'''\n\t{test_multiplication.__name__} returned integer'''	)
      with pytest.raises(lowercase	):
         matop.add(lowercase							,     lowercase	)
   elif (np.array(lowercase	)).shape == (np.array(lowercase	)).shape:
      logger.info(f'''\n\t{test_multiplication.__name__} meets dim requirements'''	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =(np.matmul(lowercase							,     lowercase	)).tolist()
      SCREAMING_SNAKE_CASE_:	List[str]            =matop.multiply(lowercase							,     lowercase	)
      assert theo == act
   else:
      with pytest.raises(lowercase	):
         logger.info(
             f'''\n\t{test_multiplication.__name__} does not meet dim requirements'''	)
         assert matop.subtract(lowercase							,     lowercase	)





@pytest.mark.mat_ops
def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =(3.5 * np.array(lowercase	)).tolist()
   SCREAMING_SNAKE_CASE_:	Tuple            =matop.scalar_multiply(lowercase							,     3.5	)
   assert theo == act





@pytest.mark.mat_ops
def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	int            =(np.identity(5	)).tolist()
   SCREAMING_SNAKE_CASE_:	Dict            =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        __magic_name__			(							lowercase	):
   if (np.array(lowercase	)).shape < (2, 2):
      with pytest.raises(lowercase	):
         logger.info(f'''\n\t{test_transpose.__name__} returned integer'''	)
         matop.transpose(lowercase	)
   else:
      SCREAMING_SNAKE_CASE_:	List[Any]            =(np.transpose(lowercase	)).tolist()
      SCREAMING_SNAKE_CASE_:	List[Any]            =matop.transpose(lowercase							,     return_map=lowercase	)
      assert theo == act








"""simple docstring"""
import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			int         =   'hybrid-clip'
  UpperCamelCase		:			Optional[int]         =   True



  def __init__(  self	:   List[Any]    ,						lowerCAmelCase	:   Any=512    ,						**lowerCAmelCase	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase						)

     if "text_config" not in kwargs:
        raise ValueError("""`text_config` can not be `None`."""						)

     if "vision_config" not in kwargs:
        raise ValueError("""`vision_config` can not be `None`."""						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =kwargs.pop("""text_config"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =kwargs.pop("""vision_config"""						)

     SCREAMING_SNAKE_CASE_:	int            =text_config.pop("""model_type"""						)
     SCREAMING_SNAKE_CASE_:	str            =vision_config.pop("""model_type"""						)

     from transformers import AutoConfig

     SCREAMING_SNAKE_CASE_:	Optional[int]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     if vision_model_type == "clip":
        SCREAMING_SNAKE_CASE_:	Tuple            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						).vision_config
     elif vision_model_type == "clip_vision_model":
        from transformers import CLIPVisionConfig

        SCREAMING_SNAKE_CASE_:	List[str]            =CLIPVisionConfig(**lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Any            =projection_dim
     SCREAMING_SNAKE_CASE_:	int            =1.0



  @classmethod
  def 		lowerCamelCase__    (  cls	:   List[Any]    ,						lowerCAmelCase	:   PretrainedConfig    ,						lowerCAmelCase	:   PretrainedConfig    ,						**lowerCAmelCase	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**lowerCAmelCase						)





  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =copy.deepcopy(self.__dict__						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.text_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.vision_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.__class__.model_type
     return output



