




import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
  )
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
  __lowerCAmelCase		:    Tuple	      =		False




class    snake_case__							:
    """simple docstring"""



    def __init__(				self    :					Optional[Any] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					List[str]=14 ,				__lowerCamelCase    :					List[Any]=7 ,				__lowerCamelCase    :					int=True ,				__lowerCamelCase    :					Optional[Any]=False ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					Optional[int]=True ,				__lowerCamelCase    :					Dict=True ,				__lowerCamelCase    :					List[Any]=99 ,				__lowerCamelCase    :					str=32 ,				__lowerCamelCase    :					Optional[Any]=5 ,				__lowerCamelCase    :					List[Any]=37 ,				__lowerCamelCase    :					str="gelu" ,				__lowerCamelCase    :					str=0.1 ,				__lowerCamelCase    :					List[str]=0.1 ,				__lowerCamelCase    :					Any=5_12 ,				__lowerCamelCase    :					Optional[Any]=16 ,				__lowerCamelCase    :					Optional[int]=2 ,				__lowerCamelCase    :					List[str]=3 ,				__lowerCamelCase    :					List[str]=4 ,				__lowerCamelCase    :					List[Any]=None ,				) ->					Optional[int]:
       a   =      parent
       a   =      batch_size
       a   =      seq_length
       a   =      is_training
       a   =      use_token_type_ids
       a   =      use_input_mask
       a   =      use_labels
       a   =      use_mc_token_ids
       a   =      vocab_size
       a   =      hidden_size
       a   =      num_hidden_layers
       a   =      intermediate_size
       a   =      hidden_act
       a   =      hidden_dropout_prob
       a   =      attention_probs_dropout_prob
       a   =      max_position_embeddings
       a   =      type_vocab_size
       a   =      type_sequence_label_size
       a   =      num_labels
       a   =      num_choices
       a   =      scope
       a   =      vocab_size - 1
       a   =      vocab_size - 1
       a   =      vocab_size - 1





    def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Dict:
       return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"							)





    def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					List[str]=False ,				__lowerCamelCase    :					Optional[Any]=False ,				__lowerCamelCase    :					Tuple=False							) ->					Any:
       a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

       a   =      None
       if self.use_input_mask:
          a   =      random_attention_mask([self.batch_size, self.seq_length]							)

       a   =      None
       if self.use_token_type_ids:
          a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size							)

       a   =      None
       if self.use_mc_token_ids:
          a   =      ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length							)

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

       a   =      self.get_config(
           gradient_checkpointing=__lowerCamelCase ,				scale_attn_by_inverse_layer_idx=__lowerCamelCase ,				reorder_and_upcast_attn=__lowerCamelCase ,				)

       return (
           config,
           input_ids,
           input_mask,
           None,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )





    def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					int=False ,				__lowerCamelCase    :					str=False ,				__lowerCamelCase    :					Tuple=False							) ->					List[str]:
       return RwkvConfig(
           vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				intermediate_size=self.intermediate_size ,				activation_function=self.hidden_act ,				resid_pdrop=self.hidden_dropout_prob ,				attn_pdrop=self.attention_probs_dropout_prob ,				n_positions=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				use_cache=__lowerCamelCase ,				bos_token_id=self.bos_token_id ,				eos_token_id=self.eos_token_id ,				pad_token_id=self.pad_token_id ,				gradient_checkpointing=__lowerCamelCase ,				scale_attn_by_inverse_layer_idx=__lowerCamelCase ,				reorder_and_upcast_attn=__lowerCamelCase ,				)





    def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
       a   =      self.get_config()
       a   =      3_00
       return config





    def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Tuple:
       (
           (
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,				
       )   =      self.prepare_config_and_inputs()

       a   =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
       a   =      ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2							)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )





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

       a   =      model(__lowerCamelCase							)

       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)
       self.parent.assertEqual(len(result.hidden_states							) ,				config.num_hidden_layers + 1							)





    def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					str ,				*__lowerCamelCase    :					Optional[int]							) ->					Dict:
       a   =      RwkvForCausalLM(__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       model.eval()

       a   =      model(__lowerCamelCase ,				labels=__lowerCamelCase							)
       self.parent.assertEqual(result.loss.shape ,				()							)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)





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

       a   =      model(__lowerCamelCase							)
       a   =      outputs.last_hidden_state

       a   =      model(input_ids[:, :2]							)
       a   =      outputs.last_hidden_state

       # Using the state computed on the first inputs, we will get the same output
       a   =      model(input_ids[:, 2:] ,				state=outputs.state							)
       a   =      outputs.last_hidden_state

       self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two] ,				dim=1							) ,				__lowerCamelCase ,				atol=1e-5							)							)





    def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				*__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Dict=False							) ->					Optional[int]:
       a   =      RwkvForCausalLM(__lowerCamelCase							)
       model.to(__lowerCamelCase							)
       if gradient_checkpointing:
          model.gradient_checkpointing_enable()

       a   =      model(__lowerCamelCase ,				labels=__lowerCamelCase							)
       self.parent.assertEqual(result.loss.shape ,				()							)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)
       result.loss.backward()





    def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					List[Any]:
       a   =      self.prepare_config_and_inputs()

       (
           (
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,		(
           a		
       )   ,				
       )   =      config_and_inputs

       a   =      {"input_ids": input_ids}

       return config, inputs_dict




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0   ,	reason="""See https://github.com/huggingface/transformers/pull/24204"""       )
@require_torch
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
    """simple docstring"""



    SCREAMING_SNAKE_CASE_	:							List[Any]										= (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE_	:							Any										= (
        {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
    )
    # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE_	:							Optional[int]										= False
    SCREAMING_SNAKE_CASE_	:							Optional[int]										= False
    SCREAMING_SNAKE_CASE_	:							Union[str, Any]										= False
    SCREAMING_SNAKE_CASE_	:							Tuple										= False
    SCREAMING_SNAKE_CASE_	:							List[str]										= False  # Rwkv does not support head masking





    def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Dict:
       a   =      RwkvModelTester(self							)
       a   =      ConfigTester(
           self ,				config_class=__lowerCamelCase ,				n_embd=37 ,				common_properties=["hidden_size", "num_hidden_layers"]							)





    def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any]=None							) ->					int:
       if isinstance(__lowerCamelCase ,				torch.Tensor							):
          a   ,		a   =      member.max().item(), member.min().item()
       elif isinstance(__lowerCamelCase ,				__lowerCamelCase							) or isinstance(__lowerCamelCase ,				__lowerCamelCase							):
          a   ,		a   =      max(__lowerCamelCase							), min(__lowerCamelCase							)

       if not isinstance(__lowerCamelCase ,				__lowerCamelCase							):
          raise TypeError("container should be a list or tuple"							)
       elif len(__lowerCamelCase							) != 2:
          raise ValueError("container should have 2 elements"							)

       a   ,		a   =      container

       a   =      (min_value >= expected_min) and (max_value <= expected_max)

       if not is_inside_interval:
          a   =      "%s not found in %s" % (safe_repr(__lowerCamelCase							), safe_repr(__lowerCamelCase							))
          self.fail(self._formatMessage(__lowerCamelCase ,				__lowerCamelCase							)							)





    def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
       self.config_tester.run_common_tests()





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





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





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





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

       for model_class in self.all_model_classes:
          a   =      model_class(config=__lowerCamelCase							)
          for name, param in model.named_parameters():
             if "time_decay" in name:
                if param.requires_grad:
                   self.assertTrue(param.data.max().item() == 3.0							)
                   self.assertTrue(param.data.min().item() == -5.0							)
             elif "time_first" in name:
                if param.requires_grad:
                   # check if it's a ones like
                   self.assertTrue(torch.allclose(param.data ,				torch.ones_like(param.data							) ,				atol=1e-5 ,				rtol=1e-5							)							)
             elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]							):
                if param.requires_grad:
                   self.assertInterval(
                       param.data ,				[0.0, 1.0] ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)
             elif "time_mix_value" in name:
                if param.requires_grad:
                   self.assertInterval(
                       param.data ,				[0.0, 1.3] ,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)





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

       a   =      getattr(self.model_tester ,				"seq_length" ,				__lowerCamelCase							)

       for model_class in self.all_model_classes:
          a   =      True
          a   =      False
          a   =      True
          a   =      model_class(__lowerCamelCase							)
          model.to(__lowerCamelCase							)
          model.eval()

          a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
          a   =      inputs["input_ids"].shape[0]
          with torch.no_grad():
             a   =      model(**__lowerCamelCase							)
          a   =      outputs.attentions
          self.assertEqual(len(__lowerCamelCase							) ,				self.model_tester.num_hidden_layers							)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          a   =      True
          a   =      model_class(__lowerCamelCase							)
          model.to(__lowerCamelCase							)
          model.eval()

          a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
          a   =      inputs["input_ids"].shape[0]
          with torch.no_grad():
             a   =      model(**__lowerCamelCase							)
          a   =      outputs.attentions
          self.assertEqual(len(__lowerCamelCase							) ,				self.model_tester.num_hidden_layers							)

          self.assertListEqual(
              list(attentions[0].shape[-3:]							) ,				[batch_size, seq_len, config.hidden_size] ,				)
          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()

          a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
          a   =      inputs["input_ids"].shape[0]
          with torch.no_grad():
             a   =      model(**__lowerCamelCase							)

          a   =      1
          self.assertEqual(out_len + added_hidden_states ,				len(__lowerCamelCase							)							)

          a   =      outputs.attentions

          self.assertEqual(len(__lowerCamelCase							) ,				self.model_tester.num_hidden_layers							)
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]							) ,				[batch_size, seq_len, config.hidden_size] ,				)





    @slow
    def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Dict:
       for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a   =      RwkvModel.from_pretrained(__lowerCamelCase							)
          self.assertIsNotNone(__lowerCamelCase							)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0   ,	reason="""See https://github.com/huggingface/transformers/pull/24204"""       )
@slow
class    snake_case__							(unittest.TestCase       ):
    """simple docstring"""



    def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Tuple:
       a   =      "RWKV/rwkv-4-169m-pile"
       a   =      AutoTokenizer.from_pretrained(self.model_id							)





    def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Dict:
       a   =      "Hello my name is Jasmine and I am a newbie to the"
       a   =      RwkvForCausalLM.from_pretrained(self.model_id							).to(__lowerCamelCase							)

       a   =      self.tokenizer("Hello my name is" ,				return_tensors="pt"							).input_ids.to(__lowerCamelCase							)
       a   =      model.generate(__lowerCamelCase ,				max_new_tokens=10							)
       a   =      self.tokenizer.decode(output[0].tolist()							)

       self.assertEqual(__lowerCamelCase ,				__lowerCamelCase							)





    def 							__UpperCAmelCase							(				self    :					str							) ->					Tuple:
       a   =      "Hello my name is Jasmine and I am a newbie to the"

       a   =      self.tokenizer("Hello my name is" ,				return_tensors="pt"							).input_ids.to(__lowerCamelCase							)
       a   =      RwkvForCausalLM.from_pretrained(self.model_id ,				torch_dtype=torch.bfloataa							).to(__lowerCamelCase							)

       a   =      model.generate(__lowerCamelCase ,				max_new_tokens=10							)
       a   =      self.tokenizer.decode(output[0].tolist()							)

       self.assertEqual(__lowerCamelCase ,				__lowerCamelCase							)












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()






