"""simple docstring"""




from typing import Any

import numpy as np
class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					None:
													# Check if RHS is negative
													if np.any(tableau[:, -1]		,where=tableau[:, -1] < 0  ):
																				raise ValueError('RHS must be > 0'  )

													A__						  =				tableau
													A__  ,	A__						  =				tableau.shape

													# Number of decision variables x1, x2, x3...
													A__						  =				n_vars

													# Number of artificial variables to be minimised
													A__						  =				len(np.where(tableau[self.n_vars : -1] == -1  )[0]  )

													# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
													A__						  =				(self.n_art_vars > 0) + 1

													# Number of slack variables added to make inequalities into equalities
													A__						  =				self.n_rows - self.n_stages

													# Objectives for each stage
													A__						  =				['max']

													# In two stage simplex, first minimise then maximise
													if self.n_art_vars:
																				self.objectives.append('min'  )

													A__						  =				['']

													# Index of current pivot row and column
													A__						  =				None
													A__						  =				None

													# Does objective row only contain (non)-negative values?
													A__						  =				False





						@staticmethod
						def 					snake_case__						(							*__UpperCAmelCase  )       ->					list[str]:
													if len(__UpperCAmelCase  ) != 3:
																				raise ValueError('Must provide n_vars, n_slack, and n_art_vars'  )

													if not all(x >= 0 and isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) for x in args  ):
																				raise ValueError('All arguments must be non-negative integers'  )

													# decision | slack | artificial
													A__						  =				['x', 's', 'a']
													A__						  =				[]
													for i in range(3  ):
																				for j in range(args[i]  ):
																											titles.append(string_starts[i] + str(j + 1  )  )
													titles.append('RHS'  )
													return titles





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					tuple[Any, Any]:
													A__						  =				self.objectives[-1]

													# Find entries of highest magnitude in objective rows
													A__						  =				(objective == 'min') - (objective == 'max')
													A__						  =				np.argmax(sign * tableau[0, : self.n_vars]  )

													# Choice is only valid if below 0 for maximise, and above for minimise
													if sign * self.tableau[0, col_idx] <= 0:
																				A__						  =				True
																				return 0, 0

													# Pivot row is chosen as having the lowest quotient when elements of
													# the pivot column divide the right-hand side

													# Slice excluding the objective rows
													A__						  =				slice(self.n_stages		,self.n_rows  )

													# RHS
													A__						  =				tableau[s, -1]

													# Elements of pivot column within slice
													A__						  =				tableau[s, col_idx]

													# Array filled with nans
													A__						  =				np.full(self.n_rows - self.n_stages		,np.nan  )

													# If element in pivot column is greater than zeron_stages, return
													# quotient or nan otherwise
													A__						  =				np.divide(__UpperCAmelCase		,__UpperCAmelCase		,out=__UpperCAmelCase		,where=divisor > 0  )

													# Arg of minimum quotient excluding the nan values. n_stages is added
													# to compensate for earlier exclusion of objective columns
													A__						  =				np.nanargmin(__UpperCAmelCase  ) + self.n_stages
													return row_idx, col_idx





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					np.ndarray:
													A__						  =				tableau[row_idx].copy()

													A__						  =				piv_row[col_idx]

													# Entry becomes 1
													piv_row *= 1 / piv_val

													# Variable in pivot column becomes basic, ie the only non-zero entry
													for idx, coeff in enumerate(tableau[:, col_idx]  ):
																				tableau[idx] += -coeff * piv_row
													A__						  =				piv_row
													return tableau





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					np.ndarray:
													self.objectives.pop()

													if not self.objectives:
																				return tableau

													# Slice containing ids for artificial columns
													A__						  =				slice(-self.n_art_vars - 1		,-1  )

													# Delete the artificial variable columns
													A__						  =				np.delete(__UpperCAmelCase		,__UpperCAmelCase		,axis=1  )

													# Delete the objective row of the first stage
													A__						  =				np.delete(__UpperCAmelCase		,0		,axis=0  )

													A__						  =				1
													self.n_rows -= 1
													A__						  =				0
													A__						  =				False
													return tableau





						def 					snake_case__						(							self  )       ->					dict[Any, Any]:
													# Stop simplex algorithm from cycling.
													for _ in range(1_00  ):
																				# Completion of each stage removes an objective. If both stages
																				# are complete, then no objectives are left
																				if not self.objectives:
																											A__						  =				self.generate_col_titles(
																											    self.n_vars		,self.n_slack		,self.n_art_vars  )

																											# Find the values of each variable at optimal solution
																											return self.interpret_tableau(self.tableau		,self.col_titles  )

																				A__  ,	A__						  =				self.find_pivot(self.tableau  )

																				# If there are no more negative values in objective row
																				if self.stop_iter:
																											# Delete artificial variable columns and rows. Update attributes
																											A__						  =				self.change_stage(self.tableau  )
																				else:
																											A__						  =				self.pivot(self.tableau		,__UpperCAmelCase		,__UpperCAmelCase  )
													return {}





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					dict[str, float]:
													A__						  =				{'P': abs(tableau[0, -1]  )}

													for i in range(self.n_vars  ):
																				# Gives ids of nonzero entries in the ith column
																				A__						  =				np.nonzero(tableau[:, i]  )
																				A__						  =				len(nonzero[0]  )

																				# First entry in the nonzero ids
																				A__						  =				nonzero[0][0]
																				A__						  =				tableau[nonzero_rowidx, i]

																				# If there is only one nonzero value in column, which is one
																				if n_nonzero == nonzero_val == 1:
																											A__						  =				tableau[nonzero_rowidx, -1]
																											A__						  =				rhs_val

        # Check for basic variables
													for title in col_titles:
																				# Don't add RHS or slack variables to output dict
																				if title[0] not in "R-s-a":
																											output_dict.setdefault(__UpperCAmelCase		,0  )
													return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()






"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class      UpperCamelCase__(      __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Tuple    				=		KandinskyPipeline
						lowerCAmelCase__	:    int    				=		[
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						lowerCAmelCase__	:    str    				=		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						lowerCAmelCase__	:    Any    				=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__	:    Dict    				=		False





						@property
						def 					snake_case__						(							self  )       ->					List[Any]:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Any:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Dict:
													return self.time_input_dim





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





						@property
						def 					snake_case__						(							self  )       ->					List[str]:
													return 1_00





						@property
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
													return tokenizer





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													torch.manual_seed(0  )
													A__						  =				MCLIPConfig(
													    numDims=self.cross_attention_dim		,transformerDimensions=self.text_embedder_hidden_size		,hidden_size=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_hidden_layers=5		,vocab_size=10_05		,)

													A__						  =				MultilingualCLIP(__UpperCAmelCase  )
													A__						  =				text_encoder.eval()

													return text_encoder





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													torch.manual_seed(0  )

													A__						  =				{
													    'in_channels': 4,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 8,
													    'addition_embed_type': 'text_image',
													    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
													    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
													    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
													    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    'layers_per_block': 1,
													    'encoder_hid_dim': self.text_embedder_hidden_size,
													    'encoder_hid_dim_type': 'text_image_proj',
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': None,
													}

													A__						  =				UNetaDConditionModel(**__UpperCAmelCase  )
													return model





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}





						@property
						def 					snake_case__						(							self  )       ->					Any:
													torch.manual_seed(0  )
													A__						  =				VQModel(**self.dummy_movq_kwargs  )
													return model





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.dummy_text_encoder
													A__						  =				self.dummy_tokenizer
													A__						  =				self.dummy_unet
													A__						  =				self.dummy_movq

													A__						  =				DDIMScheduler(
													    num_train_timesteps=10_00		,beta_schedule='linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,steps_offset=1		,prediction_type='epsilon'		,thresholding=__UpperCAmelCase		,)

													A__						  =				{
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=0  )       ->					Optional[Any]:
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(__UpperCAmelCase  )  ).to(__UpperCAmelCase  )
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1  )  ).to(__UpperCAmelCase  )
													if str(__UpperCAmelCase  ).startswith('mps'  ):
																				A__						  =				torch.manual_seed(__UpperCAmelCase  )
													else:
																				A__						  =				torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
													A__						  =				{
													    'prompt': 'horse',
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				'cpu'

													A__						  =				self.get_dummy_components()

													A__						  =				self.pipeline_class(**__UpperCAmelCase  )
													A__						  =				pipe.to(__UpperCAmelCase  )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
													A__						  =				output.images

													A__						  =				pipe(
													    **self.get_dummy_inputs(__UpperCAmelCase  )		,return_dict=__UpperCAmelCase		,)[0]

													A__						  =				image[0, -3:, -3:, -1]
													A__						  =				image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													A__						  =				np.array(
													    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]  )

													assert (
													    np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class      UpperCamelCase__(      unittest.TestCase			):





						def 					snake_case__						(							self  )       ->					Tuple:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

													A__						  =				KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'		,torch_dtype=torch.floataa  )
													pipe_prior.to(__UpperCAmelCase  )

													A__						  =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,torch_dtype=torch.floataa  )
													A__						  =				pipeline.to(__UpperCAmelCase  )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				'red cat, 4k photo'

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__  ,	A__						  =				pipe_prior(
													    __UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=5		,negative_prompt=''		,).to_tuple()

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__						  =				pipeline(
													    __UpperCAmelCase		,image_embeds=__UpperCAmelCase		,negative_image_embeds=__UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=1_00		,output_type='np'		,)

													A__						  =				output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(__UpperCAmelCase		,__UpperCAmelCase  )





