




from typing import Any

import numpy as np





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	Tuple       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	str		):

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

       SCREAMING_SNAKE_CASE_					=    tableau
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    tableau.shape

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

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

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

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

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

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

       SCREAMING_SNAKE_CASE_					=    ['']

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

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



 @staticmethod
 def    lowerCAmelCase_						(							*_lowerCAmelCase						:	int		):

       if len(__SCREAMING_SNAKE_CASE		) != 3:
             raise ValueError('Must provide n_vars, n_slack, and n_art_vars'		)

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

       # decision | slack | artificial
       SCREAMING_SNAKE_CASE_					=    ['x', 's', 'a']
       SCREAMING_SNAKE_CASE_					=    []
       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    lowerCAmelCase_						(							self						:	Optional[Any]       ,       _lowerCAmelCase						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    self.objectives[-1]

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

       # RHS
       SCREAMING_SNAKE_CASE_					=    tableau[s, -1]

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

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

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



 def    lowerCAmelCase_						(							self						:	Tuple       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    tableau[row_idx].copy()

       SCREAMING_SNAKE_CASE_					=    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
       SCREAMING_SNAKE_CASE_					=    piv_row
       return tableau



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Dict		):
       self.objectives.pop()

       if not self.objectives:
             return tableau

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

       # Delete the artificial variable columns
       SCREAMING_SNAKE_CASE_					=    np.delete(__SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE       ,       axis=1		)

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

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



 def    lowerCAmelCase_						(							self						:	Any		):

       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:
                   SCREAMING_SNAKE_CASE_					=    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		)

             SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    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
                   SCREAMING_SNAKE_CASE_					=    self.change_stage(self.tableau		)
             else:
                   SCREAMING_SNAKE_CASE_					=    self.pivot(self.tableau       ,       __SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE		)
       return {}




 def    lowerCAmelCase_						(							self						:	Optional[Any]       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	str		):
       SCREAMING_SNAKE_CASE_					=    {'P': abs(tableau[0, -1]		)}

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

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase__				:     int			       =							False





@nightly
@require_torch_gpu
class  lowerCamelCase_							(   unittest.TestCase      ):


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowerCAmelCase		)

       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    generator.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"




 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    'cyberpunk 2077'
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt=_lowerCAmelCase       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='numpy'       ,       ).images

       SCREAMING_SNAKE_CASE_					=    image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]		)

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