





from typing import Any

import numpy as np




class 	_UpperCAmelCase						:




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

							UpperCamelCase__								=						tableau
							UpperCamelCase__	,      UpperCamelCase__								=						tableau.shape

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

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

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

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

							# Objectives for each stage
							UpperCamelCase__								=						["""max"""]

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

							UpperCamelCase__								=						[""""""]

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

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




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

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

							# decision | slack | artificial
							UpperCamelCase__								=						["""x""", """s""", """a"""]
							UpperCamelCase__								=						[]
							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     ,   __lowerCAmelCase							)    ->	tuple[Any, Any]:
							UpperCamelCase__								=						self.objectives[-1]

							# Find entries of highest magnitude in objective rows
							UpperCamelCase__								=						(objective == """min""") - (objective == """max""")
							UpperCamelCase__								=						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:
									UpperCamelCase__								=						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
							UpperCamelCase__								=						slice(self.n_stages     ,   self.n_rows							)

							# RHS
							UpperCamelCase__								=						tableau[s, -1]

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

							# Array filled with nans
							UpperCamelCase__								=						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
							UpperCamelCase__								=						np.divide(__lowerCAmelCase     ,   __lowerCAmelCase     ,   out=__lowerCAmelCase     ,   where=divisor > 0							)

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




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	np.ndarray:
							UpperCamelCase__								=						tableau[row_idx].copy()

							UpperCamelCase__								=						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
							UpperCamelCase__								=						piv_row
							return tableau




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	np.ndarray:
							self.objectives.pop()

							if not self.objectives:
									return tableau

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

							# Delete the artificial variable columns
							UpperCamelCase__								=						np.delete(__lowerCAmelCase     ,   __lowerCAmelCase     ,   axis=1							)

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

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




					def 						snake_case_		(    self							)    ->	dict[Any, Any]:
							# Stop simplex algorithm from cycling.
							for _ in range(100							):
									# Completion of each stage removes an objective. If both stages
									# are complete, then no objectives are left
									if not self.objectives:
											UpperCamelCase__								=						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							)

									UpperCamelCase__	,      UpperCamelCase__								=						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
											UpperCamelCase__								=						self.change_stage(self.tableau							)
									else:
											UpperCamelCase__								=						self.pivot(self.tableau     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
							return {}




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	dict[str, float]:
							UpperCamelCase__								=						{"""P""": abs(tableau[0, -1]							)}

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

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

									# If there is only one nonzero value in column, which is one
									if n_nonzero == nonzero_val == 1:
											UpperCamelCase__								=						tableau[nonzero_rowidx, -1]
											UpperCamelCase__								=						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(__lowerCAmelCase     ,   0							)
							return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()












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						( _lowercase			,							unittest.TestCase       ):
					__A        = KandinskyPipeline
					__A        = [
					    "prompt",
					    "image_embeds",
					    "negative_image_embeds",
					]
					__A        = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
					__A        = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					__A        = False




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




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							return 32




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




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return self.time_input_dim * 4




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							return 100




					@property
					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
							return tokenizer




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						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=1_005     ,   )

							UpperCamelCase__								=						MultilingualCLIP(__lowerCAmelCase							)
							UpperCamelCase__								=						text_encoder.eval()

							return text_encoder




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

							UpperCamelCase__								=						{
							    """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,
							}

							UpperCamelCase__								=						UNetaDConditionModel(**__lowerCAmelCase							)
							return model




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							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							)    ->	Optional[int]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						VQModel(**self.dummy_movq_kwargs							)
							return model




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.dummy_text_encoder
							UpperCamelCase__								=						self.dummy_tokenizer
							UpperCamelCase__								=						self.dummy_unet
							UpperCamelCase__								=						self.dummy_movq

							UpperCamelCase__								=						DDIMScheduler(
							    num_train_timesteps=1_000     ,   beta_schedule="""linear"""     ,   beta_start=0.0_0_0_8_5     ,   beta_end=0.0_1_2     ,   clip_sample=__lowerCAmelCase     ,   set_alpha_to_one=__lowerCAmelCase     ,   steps_offset=1     ,   prediction_type="""epsilon"""     ,   thresholding=__lowerCAmelCase     ,   )

							UpperCamelCase__								=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	str:
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1							)							).to(__lowerCAmelCase							)
							if str(__lowerCAmelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
							UpperCamelCase__								=						{
							    """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							)    ->	Tuple:
							UpperCamelCase__								=						"""cpu"""

							UpperCamelCase__								=						self.get_dummy_components()

							UpperCamelCase__								=						self.pipeline_class(**__lowerCAmelCase							)
							UpperCamelCase__								=						pipe.to(__lowerCAmelCase							)

							pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						pipe(**self.get_dummy_inputs(__lowerCAmelCase							)							)
							UpperCamelCase__								=						output.images

							UpperCamelCase__								=						pipe(
							    **self.get_dummy_inputs(__lowerCAmelCase							)     ,   return_dict=__lowerCAmelCase     ,   )[0]

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

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

							UpperCamelCase__								=						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							)    ->	str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

							UpperCamelCase__								=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,   torch_dtype=torch.floataa							)
							pipe_prior.to(__lowerCAmelCase							)

							UpperCamelCase__								=						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,   torch_dtype=torch.floataa							)
							UpperCamelCase__								=						pipeline.to(__lowerCAmelCase							)
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						"""red cat, 4k photo"""

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__	,      UpperCamelCase__								=						pipe_prior(
							    __lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=5     ,   negative_prompt=""""""     ,   ).to_tuple()

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__								=						pipeline(
							    __lowerCAmelCase     ,   image_embeds=__lowerCAmelCase     ,   negative_image_embeds=__lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=100     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(__lowerCAmelCase     ,   __lowerCAmelCase							)





