




'''simple docstring'''





from typing import Any

import numpy as np




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      if np.any(tableau[:, -1]		,     where=tableau[:, -1] < 0 ):
          raise ValueError("""RHS must be > 0""" )

      _a				:							Any							       =      tableau
      _a       , _a				:							int							       =      tableau.shape

      # Number of decision variables x1, x2, x3...
      _a				:							Any							       =      n_vars

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

      # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
      _a				:							List[str]							       =      (self.n_art_vars > 0) + 1

      # Number of slack variables added to make inequalities into equalities
      _a				:							Optional[Any]							       =      self.n_rows - self.n_stages

      # Objectives for each stage
      _a				:							List[Any]							       =      ["""max"""]

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

      _a				:							List[str]							       =      [""""""]

      # Index of current pivot row and column
      _a				:							List[str]							       =      None
      _a				:							List[str]							       =      None

      # Does objective row only contain (non)-negative values?
      _a				:							List[Any]							       =      False



  @staticmethod
  def     a_							(			*__lowerCAmelCase ):







      '''simple docstring'''


      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
      _a				:							Optional[Any]							       =      ["""x""", """s""", """a"""]
      _a				:							Any							       =      []
      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     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.objectives[-1]

      # Find entries of highest magnitude in objective rows
      _a				:							Union[str, Any]							       =      (objective == """min""") - (objective == """max""")
      _a				:							Tuple							       =      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				:							Any							       =      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				:							int							       =      slice(self.n_stages		,     self.n_rows )

      # RHS
      _a				:							int							       =      tableau[s, -1]

      # Elements of pivot column within slice
      _a				:							Optional[Any]							       =      tableau[s, col_idx]

      # Array filled with nans
      _a				:							Dict							       =      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				:							Optional[Any]							       =      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
      _a				:							Any							       =      np.nanargmin(__lowerCAmelCase ) + self.n_stages
      return row_idx, col_idx



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      tableau[row_idx].copy()

      _a				:							Dict							       =      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				:							str							       =      piv_row
      return tableau



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      self.objectives.pop()

      if not self.objectives:
          return tableau

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

      # Delete the artificial variable columns
      _a				:							Union[str, Any]							       =      np.delete(__lowerCAmelCase		,     __lowerCAmelCase		,     axis=1 )

      # Delete the objective row of the first stage
      _a				:							Optional[Any]							       =      np.delete(__lowerCAmelCase		,     0		,     axis=0 )

      _a				:							int							       =      1
      self.n_rows -= 1
      _a				:							Union[str, Any]							       =      0
      _a				:							Any							       =      False
      return tableau



  def     a_							(			self ):







      '''simple docstring'''


      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:
              _a				:							Union[str, Any]							       =      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				:							Dict							       =      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				:							Optional[Any]							       =      self.change_stage(self.tableau )
          else:
              _a				:							List[str]							       =      self.pivot(self.tableau		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return {}



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      {"""P""": abs(tableau[0, -1] )}

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

          # First entry in the nonzero ids
          _a				:							List[Any]							       =      nonzero[0][0]
          _a				:							Tuple							       =      tableau[nonzero_rowidx, i]

          # If there is only one nonzero value in column, which is one
          if n_nonzero == nonzero_val == 1:
              _a				:							List[str]							       =      tableau[nonzero_rowidx, -1]
              _a				:							Dict							       =      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()










'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 					__UpperCAmelCase (		UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				unittest.TestCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					StableDiffusionModelEditingPipeline
      SCREAMING_SNAKE_CASE				:    str				       =					TEXT_TO_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					TEXT_TO_IMAGE_BATCH_PARAMS
      SCREAMING_SNAKE_CASE				:    Dict				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def 							lowerCamelCase     (					self						):
             torch.manual_seed(0						)
             UpperCAmelCase__	:   List[Any]												=	UNetaDConditionModel(
                 block_out_channels=(32, 64)  ,		layers_per_block=2  ,		sample_size=32  ,		in_channels=4  ,		out_channels=4  ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,		cross_attention_dim=32  ,		)
             UpperCAmelCase__	:   List[str]												=	DDIMScheduler()
             torch.manual_seed(0						)
             UpperCAmelCase__	:   str												=	AutoencoderKL(
                 block_out_channels=[32, 64]  ,		in_channels=3  ,		out_channels=3  ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,		latent_channels=4  ,		)
             torch.manual_seed(0						)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextConfig(
                 bos_token_id=0  ,		eos_token_id=2  ,		hidden_size=32  ,		intermediate_size=37  ,		layer_norm_eps=1E-05  ,		num_attention_heads=4  ,		num_hidden_layers=5  ,		pad_token_id=1  ,		vocab_size=1000  ,		)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextModel(_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

             UpperCAmelCase__	:   List[str]												=	{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Tuple												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 # Setting height and width to None to prevent OOMs on CPU.
                 '''height''': None,
                 '''width''': None,
                 '''num_inference_steps''': 2,
                 '''guidance_scale''': 6.0,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Optional[int]												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]
             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   int												=	np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

             assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	'''french fries'''
             UpperCAmelCase__	:   Any												=	sd_pipe(**_UpperCAmelCase  ,		negative_prompt=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	output.images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   str												=	np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

             assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   str												=	EulerAncestralDiscreteScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''						)
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[Any]												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   int												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   Tuple												=	np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

             assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   str												=	self.get_dummy_components()
             UpperCAmelCase__	:   List[Any]												=	PNDMScheduler()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Tuple												=	self.get_dummy_inputs(_UpperCAmelCase						)
             # the pipeline does not expect pndm so test if it raises error.
             with self.assertRaises(_UpperCAmelCase						):
                    UpperCAmelCase__	:   List[Any]												=	sd_pipe(**_UpperCAmelCase						).images




      def 							lowerCamelCase     (					self						):
             super().test_inference_batch_single_identical(expected_max_diff=5E-3						)




      def 							lowerCamelCase     (					self						):
             super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




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

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Dict												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   List[str]												=	StableDiffusionModelEditingPipeline.from_pretrained(_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing()

             UpperCAmelCase__	:   Union[str, Any]												=	self.get_inputs()
             UpperCAmelCase__	:   Union[str, Any]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Optional[int]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             UpperCAmelCase__	:   List[str]												=	np.array(
                 [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

             assert np.abs(expected_slice - image_slice						).max() < 1E-2

             # make sure image changes after editing
             pipe.edit_model('''A pack of roses'''  ,		'''A pack of blue roses'''						)

             UpperCAmelCase__	:   Optional[int]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             assert np.abs(expected_slice - image_slice						).max() > 1E-1




      def 							lowerCamelCase     (					self						):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase__	:   Dict												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   int												=	DDIMScheduler.from_pretrained(_UpperCAmelCase  ,		subfolder='''scheduler'''						)
             UpperCAmelCase__	:   int												=	StableDiffusionModelEditingPipeline.from_pretrained(
                 _UpperCAmelCase  ,		scheduler=_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             UpperCAmelCase__	:   Tuple												=	pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing(1						)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase__	:   Any												=	self.get_inputs()
             UpperCAmelCase__	:   Any												=	pipe(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	torch.cuda.max_memory_allocated()
             # make sure that less than 4.4 GB is allocated
             assert mem_bytes < 4.4 * 10**9