




import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
  from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor




class    snake_case__							:
    """simple docstring"""



    def __init__(				self    :					int ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Optional[int]=13 ,				__lowerCamelCase    :					Tuple=32 ,				__lowerCamelCase    :					Dict=2 ,				__lowerCamelCase    :					int=3 ,				__lowerCamelCase    :					List[Any]=16 ,				__lowerCamelCase    :					int=[1, 2, 1] ,				__lowerCamelCase    :					int=[2, 2, 4] ,				__lowerCamelCase    :					str=2 ,				__lowerCamelCase    :					Any=2.0 ,				__lowerCamelCase    :					Union[str, Any]=True ,				__lowerCamelCase    :					Union[str, Any]=0.0 ,				__lowerCamelCase    :					Tuple=0.0 ,				__lowerCamelCase    :					Tuple=0.1 ,				__lowerCamelCase    :					Union[str, Any]="gelu" ,				__lowerCamelCase    :					Any=False ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					Dict=0.02 ,				__lowerCamelCase    :					Any=1e-5 ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					List[Any]=None ,				__lowerCamelCase    :					Any=True ,				__lowerCamelCase    :					Optional[Any]=10 ,				__lowerCamelCase    :					Dict=8 ,				__lowerCamelCase    :					List[str]=["stage1", "stage2"] ,				__lowerCamelCase    :					str=[1, 2] ,				) ->					Tuple:
       a   =      parent
       a   =      batch_size
       a   =      image_size
       a   =      patch_size
       a   =      num_channels
       a   =      embed_dim
       a   =      depths
       a   =      num_heads
       a   =      window_size
       a   =      mlp_ratio
       a   =      qkv_bias
       a   =      hidden_dropout_prob
       a   =      attention_probs_dropout_prob
       a   =      drop_path_rate
       a   =      hidden_act
       a   =      use_absolute_embeddings
       a   =      patch_norm
       a   =      layer_norm_eps
       a   =      initializer_range
       a   =      is_training
       a   =      scope
       a   =      use_labels
       a   =      type_sequence_label_size
       a   =      encoder_stride
       a   =      out_features
       a   =      out_indices





    def 							__UpperCAmelCase							(				self    :					str							) ->					Tuple:
       a   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       a   =      None
       if self.use_labels:
          a   =      ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)

       a   =      self.get_config()

       return config, pixel_values, labels





    def 							__UpperCAmelCase							(				self    :					Tuple							) ->					List[str]:
       return SwinConfig(
           image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				embed_dim=self.embed_dim ,				depths=self.depths ,				num_heads=self.num_heads ,				window_size=self.window_size ,				mlp_ratio=self.mlp_ratio ,				qkv_bias=self.qkv_bias ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				drop_path_rate=self.drop_path_rate ,				hidden_act=self.hidden_act ,				use_absolute_embeddings=self.use_absolute_embeddings ,				path_norm=self.patch_norm ,				layer_norm_eps=self.layer_norm_eps ,				initializer_range=self.initializer_range ,				encoder_stride=self.encoder_stride ,				out_features=self.out_features ,				out_indices=self.out_indices ,				)





    def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					int							) ->					Dict:
       a   =      SwinModel(config=__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()
       a   =      model(__lowerCamelCase							)

       a   =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
       a   =      int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, expected_seq_len, expected_dim)							)





    def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Tuple							) ->					Tuple:
       a   =      SwinBackbone(config=__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()
       a   =      model(__lowerCamelCase							)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps							) ,				len(config.out_features							)							)
       self.parent.assertListEqual(list(result.feature_maps[0].shape							) ,				[self.batch_size, model.channels[0], 16, 16]							)

       # verify channels
       self.parent.assertEqual(len(model.channels							) ,				len(config.out_features							)							)

       # verify backbone works with out_features=None
       a   =      None
       a   =      SwinBackbone(config=__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()
       a   =      model(__lowerCamelCase							)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps							) ,				1							)
       self.parent.assertListEqual(list(result.feature_maps[0].shape							) ,				[self.batch_size, model.channels[-1], 4, 4]							)

       # verify channels
       self.parent.assertEqual(len(model.channels							) ,				1							)





    def 							__UpperCAmelCase							(				self    :					Dict ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Tuple							) ->					Dict:
       a   =      SwinForMaskedImageModeling(config=__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()
       a   =      model(__lowerCamelCase							)
       self.parent.assertEqual(
           result.logits.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

       # test greyscale images
       a   =      1
       a   =      SwinForMaskedImageModeling(__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()

       a   =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       a   =      model(__lowerCamelCase							)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, 1, self.image_size, self.image_size)							)





    def 							__UpperCAmelCase							(				self    :					Dict ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					List[str]							) ->					Tuple:
       a   =      self.type_sequence_label_size
       a   =      SwinForImageClassification(__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()
       a   =      model(__lowerCamelCase ,				labels=__lowerCamelCase							)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)

       # test greyscale images
       a   =      1
       a   =      SwinForImageClassification(__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()

       a   =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       a   =      model(__lowerCamelCase							)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)





    def 							__UpperCAmelCase							(				self    :					str							) ->					int:
       a   =      self.prepare_config_and_inputs()
       (
           (
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,				
       )   =      config_and_inputs
       a   =      {"pixel_values": pixel_values}
       return config, inputs_dict




@require_torch
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
    """simple docstring"""



    SCREAMING_SNAKE_CASE_	:							Optional[Any]										= (
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE_	:							Tuple										= (
        {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    SCREAMING_SNAKE_CASE_	:							Any										= True

    SCREAMING_SNAKE_CASE_	:							Any										= False
    SCREAMING_SNAKE_CASE_	:							Union[str, Any]										= False
    SCREAMING_SNAKE_CASE_	:							Dict										= False





    def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					str:
       a   =      SwinModelTester(self							)
       a   =      ConfigTester(self ,				config_class=__lowerCamelCase ,				embed_dim=37							)





    def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Optional[Any]:
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()





    def 							__UpperCAmelCase							(				self    :					int							) ->					Dict:
       return





    def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					List[Any]:
       a   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase							)





    @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."							)
    def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					str:
       pass





    def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[int]:
       super().test_training_gradient_checkpointing()





    def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Any:
       a   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase							)





    def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
       a   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase							)





    def 							__UpperCAmelCase							(				self    :					str							) ->					int:
       a   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





    @unittest.skip(reason="Swin does not use inputs_embeds"							)
    def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Dict:
       pass





    @unittest.skip(reason="Swin Transformer does not use feedforward chunking"							)
    def 							__UpperCAmelCase							(				self    :					Any							) ->					Dict:
       pass





    def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					str:
       a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          a   =      model_class(__lowerCamelCase							)
          self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)							)
          a   =      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowerCamelCase ,				nn.Linear							)							)





    def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
       a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          a   =      model_class(__lowerCamelCase							)
          a   =      inspect.signature(model.forward							)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          a   =      [*signature.parameters.keys()]

          a   =      ["pixel_values"]
          self.assertListEqual(arg_names[:1] ,				__lowerCamelCase							)





    def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[Any]:
       a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()
       a   =      True

       for model_class in self.all_model_classes:
          a   =      True
          a   =      False
          a   =      True
          a   =      model_class(__lowerCamelCase							)
          model.to(__lowerCamelCase							)
          model.eval()
          with torch.no_grad():
             a   =      model(**self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)							)
          a   =      outputs.attentions
          a   =      len(self.model_tester.depths							)
          self.assertEqual(len(__lowerCamelCase							) ,				__lowerCamelCase							)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          a   =      True
          a   =      config.window_size**2
          a   =      model_class(__lowerCamelCase							)
          model.to(__lowerCamelCase							)
          model.eval()
          with torch.no_grad():
             a   =      model(**self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)							)
          a   =      outputs.attentions
          self.assertEqual(len(__lowerCamelCase							) ,				__lowerCamelCase							)

          self.assertListEqual(
              list(attentions[0].shape[-3:]							) ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,				)
          a   =      len(__lowerCamelCase							)

          # Check attention is always last and order is fine
          a   =      True
          a   =      True
          a   =      model_class(__lowerCamelCase							)
          model.to(__lowerCamelCase							)
          model.eval()
          with torch.no_grad():
             a   =      model(**self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)							)

          # also another +1 for reshaped_hidden_states
          a   =      1 if model_class.__name__ == "SwinBackbone" else 2
          self.assertEqual(out_len + added_hidden_states ,				len(__lowerCamelCase							)							)

          a   =      outputs.attentions

          self.assertEqual(len(__lowerCamelCase							) ,				__lowerCamelCase							)

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]							) ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,				)





    def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Dict							) ->					Tuple:
       a   =      model_class(__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()

       with torch.no_grad():
          a   =      model(**self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)							)

       a   =      outputs.hidden_states

       a   =      getattr(
           self.model_tester ,				"expected_num_hidden_layers" ,				len(self.model_tester.depths							) + 1							)
       self.assertEqual(len(__lowerCamelCase							) ,				__lowerCamelCase							)

       # Swin has a different seq_length
       a   =      (
           config.patch_size
           if isinstance(config.patch_size ,				collections.abc.Iterable							)
           else (config.patch_size, config.patch_size)
       )

       a   =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]							) ,				[num_patches, self.model_tester.embed_dim] ,				)

       if not model_class.__name__ == "SwinBackbone":
          a   =      outputs.reshaped_hidden_states
          self.assertEqual(len(__lowerCamelCase							) ,				__lowerCamelCase							)

          a   ,		a   ,		a   ,		a   =      reshaped_hidden_states[0].shape
          a   =      (
              reshaped_hidden_states[0].view(__lowerCamelCase ,				__lowerCamelCase ,				height * width							).permute(0 ,				2 ,				1							)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]							) ,				[num_patches, self.model_tester.embed_dim] ,				)





    def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Optional[int]:
       a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

       a   =      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size ,				collections.abc.Iterable							)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
          a   =      True
          self.check_hidden_states_output(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          a   =      True

          self.check_hidden_states_output(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)





    def 							__UpperCAmelCase							(				self    :					Any							) ->					List[str]:
       a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()
       a   =      3

       a   =      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size ,				collections.abc.Iterable							)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       a   =      (
           config.patch_size
           if isinstance(config.patch_size ,				collections.abc.Iterable							)
           else (config.patch_size, config.patch_size)
       )

       a   =      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       a   =      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
          a   =      True
          self.check_hidden_states_output(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				(padded_height, padded_width)							)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          a   =      True
          self.check_hidden_states_output(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				(padded_height, padded_width)							)





    @slow
    def 							__UpperCAmelCase							(				self    :					str							) ->					str:
       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a   =      SwinModel.from_pretrained(__lowerCamelCase							)
          self.assertIsNotNone(__lowerCamelCase							)





    def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					int:
       a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

       a   =      _config_zero_init(__lowerCamelCase							)
       for model_class in self.all_model_classes:
          a   =      model_class(config=__lowerCamelCase							)
          for name, param in model.named_parameters():
             if "embeddings" not in name and param.requires_grad:
                self.assertIn(
                    ((param.data.mean() * 1e9).round() / 1e9).item() ,				[0.0, 1.0] ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)




@require_vision
@require_torch
class    snake_case__							(unittest.TestCase       ):
    """simple docstring"""



    @cached_property
    def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					str:
       return (
           AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							)
           if is_vision_available()
           else None
       )





    @slow
    def 							__UpperCAmelCase							(				self    :					Tuple							) ->					int:
       a   =      SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							).to(__lowerCamelCase							)
       a   =      self.default_image_processor

       a   =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       a   =      image_processor(images=__lowerCamelCase ,				return_tensors="pt"							).to(__lowerCamelCase							)

       # forward pass
       with torch.no_grad():
          a   =      model(**__lowerCamelCase							)

       # verify the logits
       a   =      torch.Size((1, 10_00)							)
       self.assertEqual(outputs.logits.shape ,				__lowerCamelCase							)
       a   =      torch.tensor([-0.0_948, -0.6_454, -0.0_921]							).to(__lowerCamelCase							)
       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)




@require_torch
class    snake_case__							(unittest.TestCase   ,	_UpperCamelCase       ):
    """simple docstring"""



    SCREAMING_SNAKE_CASE_	:							List[str]										= (SwinBackbone,) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE_	:							List[str]										= SwinConfig





    def 							__UpperCAmelCase							(				self    :					List[str]							) ->					List[str]:
       a   =      SwinModelTester(self							)












from typing import Any

import numpy as np




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					int							) ->					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 							__UpperCAmelCase							(				*__lowerCamelCase    :					int							) ->					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
							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 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray							) ->					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(__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   =      np.nanargmin(__lowerCamelCase							) + self.n_stages
							return row_idx, col_idx





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int							) ->					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 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray							) ->					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(__lowerCamelCase ,				__lowerCamelCase ,				axis=1							)

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

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





				def 							__UpperCAmelCase							(				self    :					Any							) ->					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 ,				__lowerCamelCase ,				__lowerCamelCase							)
							return {}





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					list[str]							) ->					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(__lowerCamelCase ,				0							)
							return output_dict


if __name__ == "__main__":
		import doctest

		doctest.testmod()






