




"""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

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

_SCREAMING_SNAKE_CASE						 =     logging.getLogger()
_SCREAMING_SNAKE_CASE						 =     logging.DEBUG
_SCREAMING_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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		str						)    ->       Optional[int]:
 if (np.array(__lowerCAmelCase						)).shape < (2, 2) or (np.array(__lowerCAmelCase						)).shape < (2, 2):
  with pytest.raises(__lowerCAmelCase						):
   logger.info(f"\n\t{test_addition.__name__} returned integer"						)
   matop.add(__lowerCAmelCase  ,	__lowerCAmelCase						)
 elif (np.array(__lowerCAmelCase						)).shape == (np.array(__lowerCAmelCase						)).shape:
  logger.info(f"\n\t{test_addition.__name__} with same matrix dims"						)
  _UpperCamelCase      :	Any					      =  (np.array(__lowerCAmelCase						) + np.array(__lowerCAmelCase						)).tolist()
  _UpperCamelCase      :	Dict					      =  matop.add(__lowerCAmelCase  ,	__lowerCAmelCase						)
  assert theo == act
 else:
  with pytest.raises(__lowerCAmelCase						):
   logger.info(f"\n\t{test_addition.__name__} with different matrix dims"						)
   matop.add(__lowerCAmelCase  ,	__lowerCAmelCase						)






@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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Tuple  ,	__lowerCAmelCase			:		List[Any]						)    ->       Any:
 if (np.array(__lowerCAmelCase						)).shape < (2, 2) or (np.array(__lowerCAmelCase						)).shape < (2, 2):
  with pytest.raises(__lowerCAmelCase						):
   logger.info(f"\n\t{test_subtraction.__name__} returned integer"						)
   matop.subtract(__lowerCAmelCase  ,	__lowerCAmelCase						)
 elif (np.array(__lowerCAmelCase						)).shape == (np.array(__lowerCAmelCase						)).shape:
  logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims"						)
  _UpperCamelCase      :	Dict					      =  (np.array(__lowerCAmelCase						) - np.array(__lowerCAmelCase						)).tolist()
  _UpperCamelCase      :	Optional[int]					      =  matop.subtract(__lowerCAmelCase  ,	__lowerCAmelCase						)
  assert theo == act
 else:
  with pytest.raises(__lowerCAmelCase						):
   logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims"						)
   assert matop.subtract(__lowerCAmelCase  ,	__lowerCAmelCase						)






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






@pytest.mark.mat_ops
def 				__lowerCAmelCase					(					)    ->       str:
 _UpperCamelCase      :	str					      =  (3.5 * np.array(__lowerCAmelCase						)).tolist()
 _UpperCamelCase      :	Optional[int]					      =  matop.scalar_multiply(__lowerCAmelCase  ,	3.5						)
 assert theo == act






@pytest.mark.mat_ops
def 				__lowerCAmelCase					(					)    ->       Union[str, Any]:
 _UpperCamelCase      :	int					      =  (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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]						)    ->       Any:
 if (np.array(__lowerCAmelCase						)).shape < (2, 2):
  with pytest.raises(__lowerCAmelCase						):
   logger.info(f"\n\t{test_transpose.__name__} returned integer"						)
   matop.transpose(__lowerCAmelCase						)
 else:
  _UpperCamelCase      :	List[str]					      =  (np.transpose(__lowerCAmelCase						)).tolist()
  _UpperCamelCase      :	int					      =  matop.transpose(__lowerCAmelCase  ,	return_map=__lowerCAmelCase						)
  assert theo == act









"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    import numpy as np
    import torch

if is_vision_available():
    from PIL import Image






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Dict=False  ,	__lowerCAmelCase			:		Optional[Any]=False  ,	__lowerCAmelCase			:		int=False						)    ->       List[str]:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 _UpperCamelCase      :	Optional[int]					      =  []
 for i in range(image_processor_tester.batch_size						):
  if equal_resolution:
   _UpperCamelCase      :	List[str]					      =  image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   _UpperCamelCase      :	int					      =  image_processor_tester.min_resolution
   if getattr(__lowerCAmelCase  ,	"size_divisor"  ,	__lowerCAmelCase						):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    _UpperCamelCase      :	Optional[int]					      =  max(image_processor_tester.size_divisor  ,	__lowerCAmelCase						)
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.random.choice(np.arange(__lowerCAmelCase  ,	image_processor_tester.max_resolution						)  ,	2						)
  image_inputs.append(
      np.random.randint(255  ,	size=(image_processor_tester.num_channels, width, height)  ,	dtype=np.uinta						)						)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _UpperCamelCase      :	Optional[Any]					      =  [Image.fromarray(np.moveaxis(__lowerCAmelCase  ,	0  ,	-1						)						) for image in image_inputs]

 if torchify:
  _UpperCamelCase      :	str					      =  [torch.from_numpy(__lowerCAmelCase						) for image in image_inputs]

 return image_inputs






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Tuple=10  ,	__lowerCAmelCase			:		str=10  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		List[Any]=False						)    ->       Optional[int]:
 _UpperCamelCase      :	Optional[int]					      =  []
 for i in range(image_processor_tester.num_frames						):
  video.append(np.random.randint(255  ,	size=(image_processor_tester.num_channels, width, height)  ,	dtype=np.uinta						)						)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _UpperCamelCase      :	Optional[int]					      =  [Image.fromarray(np.moveaxis(__lowerCAmelCase  ,	0  ,	-1						)						) for frame in video]

 if torchify:
  _UpperCamelCase      :	int					      =  [torch.from_numpy(__lowerCAmelCase						) for frame in video]

 return video






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		Union[str, Any]=False  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		Dict=False						)    ->       Any:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 _UpperCamelCase      :	Optional[Any]					      =  []
 for i in range(image_processor_tester.batch_size						):
  if equal_resolution:
   _UpperCamelCase      :	List[Any]					      =  image_processor_tester.max_resolution
  else:
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[int]					      =  np.random.choice(
       np.arange(image_processor_tester.min_resolution  ,	image_processor_tester.max_resolution						)  ,	2						)
   _UpperCamelCase      :	List[Any]					      =  prepare_video(
       image_processor_tester=__lowerCAmelCase  ,	width=__lowerCAmelCase  ,	height=__lowerCAmelCase  ,	numpify=__lowerCAmelCase  ,	torchify=__lowerCAmelCase  ,	)
  video_inputs.append(__lowerCAmelCase						)

 return video_inputs







class  _SCREAMING_SNAKE_CASE       :



 '''simple docstring'''





 __UpperCAmelCase		   =							None






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	str					      =  self.image_processing_class(**self.image_processor_dict   )
  _UpperCamelCase      :	Optional[int]					      =  json.loads(image_processor.to_json_string()   )
  for key, value in self.image_processor_dict.items():
   self.assertEqual(obj[key]					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Union[str, Any]					      =  self.image_processing_class(**self.image_processor_dict   )

  with tempfile.TemporaryDirectory() as tmpdirname:
   _UpperCamelCase      :	Tuple					      =  os.path.join(lowerCAmelCase__					,     "image_processor.json"   )
   image_processor_first.to_json_file(lowerCAmelCase__   )
   _UpperCamelCase      :	Tuple					      =  self.image_processing_class.from_json_file(lowerCAmelCase__   )

  self.assertEqual(image_processor_second.to_dict()					,     image_processor_first.to_dict()   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  self.image_processing_class(**self.image_processor_dict   )

  with tempfile.TemporaryDirectory() as tmpdirname:
   _UpperCamelCase      :	Optional[int]					      =  image_processor_first.save_pretrained(lowerCAmelCase__   )[0]
   check_json_file_has_correct_format(lowerCAmelCase__   )
   _UpperCamelCase      :	int					      =  self.image_processing_class.from_pretrained(lowerCAmelCase__   )

  self.assertEqual(image_processor_second.to_dict()					,     image_processor_first.to_dict()   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  self.image_processing_class()
  self.assertIsNotNone(lowerCAmelCase__   )






 @require_torch
 @require_vision
 def lowercase_      (self   ):

  '''simple docstring'''







  if self.test_cast_dtype is not None:
   # Initialize image_processor
   _UpperCamelCase      :	Optional[int]					      =  self.image_processing_class(**self.image_processor_dict   )

   # create random PyTorch tensors
   _UpperCamelCase      :	str					      =  prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase__					,     torchify=lowerCAmelCase__   )

   _UpperCamelCase      :	List[Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   )
   # for layoutLM compatiblity
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )

   _UpperCamelCase      :	List[Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to(torch.floataa   )
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )

   _UpperCamelCase      :	str					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to("cpu"					,     torch.bfloataa   )
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.bfloataa   )

   with self.assertRaises(lowerCAmelCase__   ):
    _UpperCamelCase      :	Optional[int]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to(torch.bfloataa					,     "cpu"   )

   # Try with text + image feature
   _UpperCamelCase      :	Union[str, Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   )
   encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]   )}   )
   _UpperCamelCase      :	Optional[int]					      =  encoding.to(torch.floataa   )

   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )
   self.assertEqual(encoding.input_ids.dtype					,     torch.long   )



