"""simple docstring"""


from typing import Any

import numpy as np






class        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										# Check if RHS is negative
										if np.any(tableau[:, -1]     ,			where=tableau[:, -1] < 0				):
																raise ValueError("RHS must be > 0"				)

										lowerCamelCase__ =tableau
										lowerCamelCase__ ,				lowerCamelCase__ =tableau.shape

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

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

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

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

										# Objectives for each stage
										lowerCamelCase__ =["max"]

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

										lowerCamelCase__ =[""]

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

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

				@staticmethod
				def 		_a     (				*_lowerCamelCase				):
										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
										lowerCamelCase__ =["x", "s", "a"]
										lowerCamelCase__ =[]
										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				):
										lowerCamelCase__ =self.objectives[-1]

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

										# RHS
										lowerCamelCase__ =tableau[s, -1]

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

										# Array filled with nans
										lowerCamelCase__ =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
										lowerCamelCase__ =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
										lowerCamelCase__ =np.nanargmin(_lowerCamelCase				) + self.n_stages
										return row_idx, col_idx

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =tableau[row_idx].copy()

										lowerCamelCase__ =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
										lowerCamelCase__ =piv_row
										return tableau

				def 		_a     (				self     ,			_lowerCamelCase				):
										self.objectives.pop()

										if not self.objectives:
																return tableau

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

										# Delete the artificial variable columns
										lowerCamelCase__ =np.delete(_lowerCamelCase     ,			_lowerCamelCase     ,			axis=1				)

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

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

				def 		_a     (				self				):
										# Stop simplex algorithm from cycling.
										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:
																						lowerCamelCase__ =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				)

																lowerCamelCase__ ,				lowerCamelCase__ =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
																						lowerCamelCase__ =self.change_stage(self.tableau				)
																else:
																						lowerCamelCase__ =self.pivot(self.tableau     ,			_lowerCamelCase     ,			_lowerCamelCase				)
										return {}






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ ={"P": abs(tableau[0, -1]				)}

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

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

																# If there is only one nonzero value in column, which is one
																if n_nonzero == nonzero_val == 1:
																						lowerCamelCase__ =tableau[nonzero_rowidx, -1]
																						lowerCamelCase__ =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"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


