






from typing import Any

import numpy as np


class 					__SCREAMING_SNAKE_CASE:
							def __init__(       self: Union[str, Any]    ,		UpperCamelCase: np.ndarray    ,		UpperCamelCase: int  )						->						None:




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

											snake_case__													=       tableau
											snake_case__													=       tableau.shape

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

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

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

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

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

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

											snake_case__													=       ['']

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

											# Does objective row only contain (non)-negative values?
											snake_case__													=       False
							@staticmethod
							def lowerCAmelCase_	(       *UpperCamelCase: int  )						->						list[str]:




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

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

											# decision | slack | artificial
											snake_case__													=       ['x', 's', 'a']
											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: Any    ,		UpperCamelCase: np.ndarray  )						->						tuple[Any, Any]:
											snake_case__													=       self.objectives[-1]

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

											# RHS
											snake_case__													=       tableau[s, -1]

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

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

											# Arg of minimum quotient excluding the nan values. n_stages is added
											# to compensate for earlier exclusion of objective columns
											snake_case__													=       np.nanargmin(lowercase_  ) + self.n_stages
											return row_idx, col_idx
							def lowerCAmelCase_	(       self: List[Any]    ,		UpperCamelCase: np.ndarray    ,		UpperCamelCase: int    ,		UpperCamelCase: int  )						->						np.ndarray:
											snake_case__													=       tableau[row_idx].copy()

											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
											snake_case__													=       piv_row
											return tableau
							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: np.ndarray  )						->						np.ndarray:
											self.objectives.pop()

											if not self.objectives:
															return tableau

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

											# Delete the artificial variable columns
											snake_case__													=       np.delete(lowercase_    ,		lowercase_    ,		axis=1  )

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

											snake_case__													=       1
											self.n_rows -= 1
											snake_case__													=       0
											snake_case__													=       False
											return tableau
							def lowerCAmelCase_	(       self: Dict  )						->						dict[Any, Any]:




											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:
																			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  )

															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
																			snake_case__													=       self.change_stage(self.tableau  )
															else:
																			snake_case__													=       self.pivot(self.tableau    ,		lowercase_    ,		lowercase_  )
											return {}


							def lowerCAmelCase_	(       self: Union[str, Any]    ,		UpperCamelCase: np.ndarray    ,		UpperCamelCase: list[str]  )						->						dict[str, float]:
											snake_case__													=       {'P': abs(tableau[0, -1]  )}

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

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()










import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
							from tokenizers import Tokenizer
							from tokenizers.models import WordPiece


class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def lowerCAmelCase_	(       self: Any    ,		UpperCamelCase: Union[str, Any]  )						->						Optional[int]:
											snake_case__													=       list(tokenizer_class.max_model_input_sizes.keys()  )
											for model_name in sa_models[:1]:
															snake_case__													=       tokenizer_class.from_pretrained(UpperCamelCase  )
															self.assertIsNotNone(UpperCamelCase  )
															self.assertIsInstance(UpperCamelCase    ,		UpperCamelCase  )
															self.assertIsInstance(UpperCamelCase    ,		UpperCamelCase  )

															for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(UpperCamelCase    ,		UpperCamelCase  )
																			snake_case__													=       tokenizer.convert_tokens_to_ids(UpperCamelCase  )
																			self.assertIsInstance(UpperCamelCase    ,		UpperCamelCase  )
							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: BatchEncoding    ,		UpperCamelCase: Optional[Callable] = None  )						->						List[str]:
											snake_case__													=       pickle.dumps(UpperCamelCase  )
											self.assertIsNotNone(UpperCamelCase  )

											snake_case__													=       pickle.loads(UpperCamelCase  )

											# Ensure is_fast is correctly restored
											self.assertEqual(be_restored.is_fast    ,		be_original.is_fast  )

											# Ensure encodings are potentially correctly restored
											if be_original.is_fast:
															self.assertIsNotNone(be_restored.encodings  )
											else:
															self.assertIsNone(be_restored.encodings  )

											# Ensure the keys are the same
											for original_v, restored_v in zip(be_original.values()    ,		be_restored.values()  ):
															if equal_op:
																			self.assertTrue(equal_op(UpperCamelCase    ,		UpperCamelCase  )  )
															else:
																			self.assertEqual(UpperCamelCase    ,		UpperCamelCase  )
							@slow
							def lowerCAmelCase_	(       self: Optional[int]  )						->						Optional[int]:
											self.check_tokenizer_from_pretrained(UpperCamelCase  )
							def lowerCAmelCase_	(       self: Optional[int]  )						->						Optional[Any]:
											self.assertEqual(TensorType('tf'  )    ,		TensorType.TENSORFLOW  )
											self.assertEqual(TensorType('pt'  )    ,		TensorType.PYTORCH  )
											self.assertEqual(TensorType('np'  )    ,		TensorType.NUMPY  )
							@require_tokenizers
							def lowerCAmelCase_	(       self: Optional[int]  )						->						Optional[Any]:
											import numpy as np

											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )
											snake_case__													=       BertTokenizerFast.from_pretrained('bert-base-cased'  )

											# Python no tensor
											with self.subTest('BatchEncoding (Python, return_tensors=None)'  ):
															self.assert_dump_and_restore(tokenizer_p('Small example to encode'  )  )

											with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'  ):
															self.assert_dump_and_restore(
															    tokenizer_p('Small example to encode'    ,		return_tensors=TensorType.NUMPY  )    ,		np.array_equal  )

											with self.subTest('BatchEncoding (Rust, return_tensors=None)'  ):
															self.assert_dump_and_restore(tokenizer_r('Small example to encode'  )  )

											with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'  ):
															self.assert_dump_and_restore(
															    tokenizer_r('Small example to encode'    ,		return_tensors=TensorType.NUMPY  )    ,		np.array_equal  )
							@require_tf
							@require_tokenizers
							def lowerCAmelCase_	(       self: Any  )						->						Optional[Any]:
											import tensorflow as tf

											def tf_array_equals(UpperCamelCase: Optional[int]    ,		UpperCamelCase: Dict  ):
															return tf.reduce_all(tf.equal(UpperCamelCase    ,		UpperCamelCase  )  )

											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )
											snake_case__													=       BertTokenizerFast.from_pretrained('bert-base-cased'  )

											with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'  ):
															self.assert_dump_and_restore(
															    tokenizer_p('Small example to encode'    ,		return_tensors=TensorType.TENSORFLOW  )    ,		UpperCamelCase  )

											with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'  ):
															self.assert_dump_and_restore(
															    tokenizer_r('Small example to encode'    ,		return_tensors=TensorType.TENSORFLOW  )    ,		UpperCamelCase  )
							@require_torch
							@require_tokenizers
							def lowerCAmelCase_	(       self: int  )						->						List[str]:
											import torch

											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )
											snake_case__													=       BertTokenizerFast.from_pretrained('bert-base-cased'  )

											with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'  ):
															self.assert_dump_and_restore(
															    tokenizer_p('Small example to encode'    ,		return_tensors=TensorType.PYTORCH  )    ,		torch.equal  )

											with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'  ):
															self.assert_dump_and_restore(
															    tokenizer_r('Small example to encode'    ,		return_tensors=TensorType.PYTORCH  )    ,		torch.equal  )
							@require_tokenizers
							def lowerCAmelCase_	(       self: Any  )						->						Optional[int]:
											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )
											snake_case__													=       BertTokenizerFast.from_pretrained('bert-base-cased'  )

											with self.subTest('Python Tokenizer'  ):
															self.assertFalse(tokenizer_p('Small example to_encode'  ).is_fast  )

											with self.subTest('Rust Tokenizer'  ):
															self.assertTrue(tokenizer_r('Small example to_encode'  ).is_fast  )
							@require_tokenizers
							def lowerCAmelCase_	(       self: Dict  )						->						Union[str, Any]:
											snake_case__													=       BertTokenizerFast.from_pretrained('bert-base-cased'  )
											snake_case__													=       tokenizer_r(['Test', '\xad', 'test']    ,		is_split_into_words=UpperCamelCase  )

											self.assertEqual(encoded.word_to_tokens(0  )    ,		TokenSpan(start=1    ,		end=2  )  )
											self.assertEqual(encoded.word_to_tokens(1  )    ,		UpperCamelCase  )
											self.assertEqual(encoded.word_to_tokens(2  )    ,		TokenSpan(start=2    ,		end=3  )  )
							def lowerCAmelCase_	(       self: Dict  )						->						Tuple:
											snake_case__													=       BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='np'  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(2, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(2,)  )
											# test converting the converted
											with CaptureStderr() as cs:
															snake_case__													=       batch.convert_to_tensors(tensor_type='np'  )
											self.assertFalse(len(cs.err  )    ,		msg=F'''should have no warning, but got {cs.err}'''  )

											snake_case__													=       BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='np'    ,		prepend_batch_axis=UpperCamelCase  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(1, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(1,)  )
							@require_torch
							def lowerCAmelCase_	(       self: List[str]  )						->						Dict:
											snake_case__													=       BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='pt'  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(2, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(2,)  )
											# test converting the converted
											with CaptureStderr() as cs:
															snake_case__													=       batch.convert_to_tensors(tensor_type='pt'  )
											self.assertFalse(len(cs.err  )    ,		msg=F'''should have no warning, but got {cs.err}'''  )

											snake_case__													=       BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='pt'    ,		prepend_batch_axis=UpperCamelCase  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(1, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(1,)  )
							@require_tf
							def lowerCAmelCase_	(       self: Any  )						->						Optional[int]:
											snake_case__													=       BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='tf'  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(2, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(2,)  )
											# test converting the converted
											with CaptureStderr() as cs:
															snake_case__													=       batch.convert_to_tensors(tensor_type='tf'  )
											self.assertFalse(len(cs.err  )    ,		msg=F'''should have no warning, but got {cs.err}'''  )

											snake_case__													=       BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='tf'    ,		prepend_batch_axis=UpperCamelCase  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(1, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(1,)  )
							@require_flax
							def lowerCAmelCase_	(       self: Optional[int]  )						->						List[str]:
											snake_case__													=       BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='jax'  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(2, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(2,)  )
											# test converting the converted
											with CaptureStderr() as cs:
															snake_case__													=       batch.convert_to_tensors(tensor_type='jax'  )
											self.assertFalse(len(cs.err  )    ,		msg=F'''should have no warning, but got {cs.err}'''  )

											snake_case__													=       BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}  )
											snake_case__													=       batch.convert_to_tensors(tensor_type='jax'    ,		prepend_batch_axis=UpperCamelCase  )
											self.assertEqual(tensor_batch['inputs'].shape    ,		(1, 3)  )
											self.assertEqual(tensor_batch['labels'].shape    ,		(1,)  )
							def lowerCAmelCase_	(       self: Union[str, Any]  )						->						str:
											snake_case__													=       [{'input_ids': np.array([0, 1, 2]  )}, {'input_ids': np.array([0, 1, 2, 3]  )}]
											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )

											snake_case__													=       tokenizer.pad(UpperCamelCase    ,		padding=UpperCamelCase  )
											self.assertTrue(isinstance(batch['input_ids']    ,		np.ndarray  )  )
											self.assertEqual(batch['input_ids'].tolist()    ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
											snake_case__													=       tokenizer.pad(UpperCamelCase    ,		padding=UpperCamelCase    ,		return_tensors='np'  )
											self.assertTrue(isinstance(batch['input_ids']    ,		np.ndarray  )  )
											self.assertEqual(batch['input_ids'].tolist()    ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
							@require_torch
							def lowerCAmelCase_	(       self: Dict  )						->						str:
											import torch

											snake_case__													=       [{'input_ids': torch.tensor([0, 1, 2]  )}, {'input_ids': torch.tensor([0, 1, 2, 3]  )}]
											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )

											snake_case__													=       tokenizer.pad(UpperCamelCase    ,		padding=UpperCamelCase  )
											self.assertTrue(isinstance(batch['input_ids']    ,		torch.Tensor  )  )
											self.assertEqual(batch['input_ids'].tolist()    ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
											snake_case__													=       tokenizer.pad(UpperCamelCase    ,		padding=UpperCamelCase    ,		return_tensors='pt'  )
											self.assertTrue(isinstance(batch['input_ids']    ,		torch.Tensor  )  )
											self.assertEqual(batch['input_ids'].tolist()    ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
							@require_tf
							def lowerCAmelCase_	(       self: Optional[int]  )						->						Optional[int]:
											import tensorflow as tf

											snake_case__													=       [{'input_ids': tf.constant([0, 1, 2]  )}, {'input_ids': tf.constant([0, 1, 2, 3]  )}]
											snake_case__													=       BertTokenizer.from_pretrained('bert-base-cased'  )

											snake_case__													=       tokenizer.pad(UpperCamelCase    ,		padding=UpperCamelCase  )
											self.assertTrue(isinstance(batch['input_ids']    ,		tf.Tensor  )  )
											self.assertEqual(batch['input_ids'].numpy().tolist()    ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
											snake_case__													=       tokenizer.pad(UpperCamelCase    ,		padding=UpperCamelCase    ,		return_tensors='tf'  )
											self.assertTrue(isinstance(batch['input_ids']    ,		tf.Tensor  )  )
											self.assertEqual(batch['input_ids'].numpy().tolist()    ,		[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
							@require_tokenizers
							def lowerCAmelCase_	(       self: Any  )						->						List[str]:
											snake_case__													=       Tokenizer(WordPiece(unk_token='[UNK]'  )  )
											PreTrainedTokenizerFast(tokenizer_object=UpperCamelCase  )


							@require_tokenizers
							def lowerCAmelCase_	(       self: Tuple  )						->						str:
											snake_case__													=       Tokenizer(WordPiece(unk_token='[UNK]'  )  )
											with tempfile.TemporaryDirectory() as tmpdirname:
															bert_tokenizer.save(os.path.join(UpperCamelCase    ,		'tokenizer.json'  )  )
															PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCamelCase    ,		'tokenizer.json'  )  )




