

import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

lowerCamelCase__			:					Tuple							  =					[[12, 10], [3, 9]]
lowerCamelCase__			:					int							  =					[[3, 4], [7, 4]]
lowerCamelCase__			:					str							  =					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
lowerCamelCase__			:					Optional[Any]							  =					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
lowerCamelCase__			:					List[str]							  =					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
lowerCamelCase__			:					List[str]							  =					[1]
lowerCamelCase__			:					int							  =					[2]

lowerCamelCase__			:					str							  =					logging.getLogger()
lowerCamelCase__			:					List[Any]							  =					logging.DEBUG
lowerCamelCase__			:					Dict							  =					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   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Dict	)			->     Optional[int]:



	if (np.array(UpperCamelCase_	)).shape < (2, 2) or (np.array(UpperCamelCase_	)).shape < (2, 2):
		with pytest.raises(UpperCamelCase_	):
			logger.info(f'''\n\t{test_addition.__name__} returned integer'''	)
			matop.add(UpperCamelCase_,					UpperCamelCase_	)
	elif (np.array(UpperCamelCase_	)).shape == (np.array(UpperCamelCase_	)).shape:
		logger.info(f'''\n\t{test_addition.__name__} with same matrix dims'''	)
		_UpperCAmelCase      :							Tuple       =   (np.array(UpperCamelCase_	) + np.array(UpperCamelCase_	)).tolist()
		_UpperCAmelCase      :							str       =   matop.add(UpperCamelCase_,					UpperCamelCase_	)
		assert theo == act
	else:
		with pytest.raises(UpperCamelCase_	):
			logger.info(f'''\n\t{test_addition.__name__} with different matrix dims'''	)
			matop.add(UpperCamelCase_,					UpperCamelCase_	)
@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   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  List[Any]	)			->     Union[str, Any]:



	if (np.array(UpperCamelCase_	)).shape < (2, 2) or (np.array(UpperCamelCase_	)).shape < (2, 2):
		with pytest.raises(UpperCamelCase_	):
			logger.info(f'''\n\t{test_subtraction.__name__} returned integer'''	)
			matop.subtract(UpperCamelCase_,					UpperCamelCase_	)
	elif (np.array(UpperCamelCase_	)).shape == (np.array(UpperCamelCase_	)).shape:
		logger.info(f'''\n\t{test_subtraction.__name__} with same matrix dims'''	)
		_UpperCAmelCase      :							Dict       =   (np.array(UpperCamelCase_	) - np.array(UpperCamelCase_	)).tolist()
		_UpperCAmelCase      :							Optional[Any]       =   matop.subtract(UpperCamelCase_,					UpperCamelCase_	)
		assert theo == act
	else:
		with pytest.raises(UpperCamelCase_	):
			logger.info(f'''\n\t{test_subtraction.__name__} with different matrix dims'''	)
			assert matop.subtract(UpperCamelCase_,					UpperCamelCase_	)
@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   (     _lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  Optional[int]	)			->     Optional[int]:



	if (np.array(UpperCamelCase_	)).shape < (2, 2) or (np.array(UpperCamelCase_	)).shape < (2, 2):
		logger.info(f'''\n\t{test_multiplication.__name__} returned integer'''	)
		with pytest.raises(UpperCamelCase_	):
			matop.add(UpperCamelCase_,					UpperCamelCase_	)
	elif (np.array(UpperCamelCase_	)).shape == (np.array(UpperCamelCase_	)).shape:
		logger.info(f'''\n\t{test_multiplication.__name__} meets dim requirements'''	)
		_UpperCAmelCase      :							Union[str, Any]       =   (np.matmul(UpperCamelCase_,					UpperCamelCase_	)).tolist()
		_UpperCAmelCase      :							Tuple       =   matop.multiply(UpperCamelCase_,					UpperCamelCase_	)
		assert theo == act
	else:
		with pytest.raises(UpperCamelCase_	):
			logger.info(
			    f'''\n\t{test_multiplication.__name__} does not meet dim requirements'''	)
			assert matop.subtract(UpperCamelCase_,					UpperCamelCase_	)
@pytest.mark.mat_ops
def  UpperCamelCase   (     )			->     str:
	_UpperCAmelCase      :							Optional[int]       =   (3.5 * np.array(UpperCamelCase_	)).tolist()
	_UpperCAmelCase      :							Dict       =   matop.scalar_multiply(UpperCamelCase_,					3.5	)
	assert theo == act
@pytest.mark.mat_ops
def  UpperCamelCase   (     )			->     List[str]:
	_UpperCAmelCase      :							Tuple       =   (np.identity(5	)).tolist()
	_UpperCAmelCase      :							Optional[Any]       =   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   (     _lowerCAmelCase       :  Union[str, Any]	)			->     List[Any]:



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








"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       _UpperCAmelCase     (						__a      ,       unittest.TestCase):
			__a	:    List[Any]			   =							KandinskyPriorPipeline
			__a	:    List[Any]			   =							["""prompt"""]
			__a	:    List[str]			   =							["""prompt""", """negative_prompt"""]
			__a	:    Tuple			   =							[
			    """num_images_per_prompt""",
			    """generator""",
			    """num_inference_steps""",
			    """latents""",
			    """negative_prompt""",
			    """guidance_scale""",
			    """output_type""",
			    """return_dict""",
			]
			__a	:    str			   =							False
			@property
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				return self.time_input_dim
			@property
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				return self.time_input_dim * 4
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				return 1_00
			@property
			def 							__snake_case	(						self       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
				return tokenizer
			@property
			def 							__snake_case	(						self       )      ->      int:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							List[Any]       =   CLIPTextConfig(
				    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=self.text_embedder_hidden_size ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=10_00 ,						)
				return CLIPTextModelWithProjection(_A       )
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				torch.manual_seed(0       )

				_UpperCAmelCase      :							Optional[int]       =   {
				    """num_attention_heads""": 2,
				    """attention_head_dim""": 12,
				    """embedding_dim""": self.text_embedder_hidden_size,
				    """num_layers""": 1,
				}

				_UpperCAmelCase      :							List[Any]       =   PriorTransformer(**_A       )
				# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
				_UpperCAmelCase      :							List[Any]       =   nn.Parameter(torch.ones(model.clip_std.shape       )       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							Optional[Any]       =   CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size ,						image_size=2_24 ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						num_attention_heads=4 ,						num_channels=3 ,						num_hidden_layers=5 ,						patch_size=14 ,						)

				_UpperCAmelCase      :							Tuple       =   CLIPVisionModelWithProjection(_A       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   CLIPImageProcessor(
				    crop_size=2_24 ,						do_center_crop=_A ,						do_normalize=_A ,						do_resize=_A ,						image_mean=[0.48145466, 0.4578275, 0.40821073] ,						image_std=[0.26862954, 0.26130258, 0.27577711] ,						resample=3 ,						size=2_24 ,						)

				return image_processor
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   self.dummy_prior
				_UpperCAmelCase      :							int       =   self.dummy_image_encoder
				_UpperCAmelCase      :							int       =   self.dummy_text_encoder
				_UpperCAmelCase      :							str       =   self.dummy_tokenizer
				_UpperCAmelCase      :							Any       =   self.dummy_image_processor

				_UpperCAmelCase      :							Any       =   UnCLIPScheduler(
				    variance_type="""fixed_small_log""" ,						prediction_type="""sample""" ,						num_train_timesteps=10_00 ,						clip_sample=_A ,						clip_sample_range=10.0 ,						)

				_UpperCAmelCase      :							Any       =   {
				    """prior""": prior,
				    """image_encoder""": image_encoder,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """scheduler""": scheduler,
				    """image_processor""": image_processor,
				}

				return components
			def 							__snake_case	(						self ,						_A ,						_A=0       )      ->      Optional[Any]:






				'''simple docstring'''







				if str(_A       ).startswith("""mps"""       ):
					_UpperCAmelCase      :							Any       =   torch.manual_seed(_A       )
				else:
					_UpperCAmelCase      :							Optional[int]       =   torch.Generator(device=_A       ).manual_seed(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   {
				    """prompt""": """horse""",
				    """generator""": generator,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   """cpu"""

				_UpperCAmelCase      :							int       =   self.get_dummy_components()

				_UpperCAmelCase      :							Optional[Any]       =   self.pipeline_class(**_A       )
				_UpperCAmelCase      :							List[Any]       =   pipe.to(_A       )

				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							str       =   pipe(**self.get_dummy_inputs(_A       )       )
				_UpperCAmelCase      :							Any       =   output.image_embeds

				_UpperCAmelCase      :							Optional[int]       =   pipe(
				    **self.get_dummy_inputs(_A       ) ,						return_dict=_A ,						)[0]

				_UpperCAmelCase      :							int       =   image[0, -10:]
				_UpperCAmelCase      :							Optional[Any]       =   image_from_tuple[0, -10:]

				assert image.shape == (1, 32)

				_UpperCAmelCase      :							Optional[Any]       =   np.array(
				    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
			@skip_mps
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   torch_device == """cpu"""
				_UpperCAmelCase      :							Optional[Any]       =   True
				_UpperCAmelCase      :							List[Any]       =   False

				self._test_inference_batch_single_identical(
				    test_max_difference=_A ,						relax_max_difference=_A ,						test_mean_pixel_difference=_A ,						)
			@skip_mps
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   torch_device == """cpu"""
				_UpperCAmelCase      :							List[str]       =   False

				self._test_attention_slicing_forward_pass(
				    test_max_difference=_A ,						test_mean_pixel_difference=_A ,						)





