
'''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()

UpperCAmelCase__   : int					=		{
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

UpperCAmelCase__   : int					=		datasets.load_dataset("xnli", split="test+validation")

UpperCAmelCase__   : str					=		0
UpperCAmelCase__   : int					=		0
UpperCAmelCase__   : Optional[int]					=		0
UpperCAmelCase__   : List[str]					=		0







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Tuple      )					->       int:







			'''simple docstring'''



			if spm_diff == list(reversed(snake_case_      )      ):
						# AAA -> AA+A vs A+AA case.
						return True
			elif len(snake_case_      ) == len(snake_case_      ) and fast.decode(snake_case_      ) == fast.decode(snake_case_      ):
						# Second order OK
						# Barrich -> Barr + ich vs Bar + rich
						return True
			lowerCAmelCase__								=							slow.encode(slow.decode(snake_case_      )      )
			lowerCAmelCase__								=							fast.encode(fast.decode(snake_case_      )      )
			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   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     str						,	UpperCamelCase_					:     int      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							fast.encode_plus(snake_case_      )[0]
			lowerCAmelCase__								=							enc.offsets
			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   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



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

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

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

			if check_LTR_mark(snake_case_						,	snake_case_						,	snake_case_      ):
						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(snake_case_      )      ):
												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]						,	snake_case_						,	snake_case_      ) and check_details(
															    snake_case_						,	spm_ids[first + i : last]						,	tok_ids[first + j : last]						,	snake_case_						,	snake_case_						,	):
																		return True

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

			lowerCAmelCase__								=							fast.decode(spm_ids[:first]      )
			lowerCAmelCase__								=							fast.decode(spm_ids[last:]      )
			lowerCAmelCase__								=							fast.decode(spm_ids[first:last]      )
			print()
			print(snake_case_      )
			return False







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     int      )					->       Union[str, Any]:







			'''simple docstring'''



			global perfect
			global imperfect
			global wrong
			global total

			lowerCAmelCase__								=							slow.encode(snake_case_      )
			lowerCAmelCase__								=							fast.encode(snake_case_      )

			lowerCAmelCase__								=							False
			total += 1

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

			if total % 1_00_00 == 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(snake_case_      )}\n{fast.tokenize(snake_case_      )}"""







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Union[str, Any]      )					->       List[str]:







			'''simple docstring'''



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

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


if __name__ == "__main__":
		for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
				UpperCAmelCase__   : Optional[int]					=		list(slow_class.max_model_input_sizes.keys())
				for checkpoint in checkpoint_names:
						UpperCAmelCase__   : int					=		0
						UpperCAmelCase__   : Optional[int]					=		0
						UpperCAmelCase__   : List[Any]					=		0
						UpperCAmelCase__   : Any					=		0

						print(F"========================== Checking {name}: {checkpoint} ==========================")
						UpperCAmelCase__   : Dict					=		slow_class.from_pretrained(checkpoint, force_download=True)
						UpperCAmelCase__   : List[Any]					=		fast_class.from_pretrained(checkpoint, force_download=True)
						test_tokenizer(slow, fast)
						print(F"Accuracy {perfect * 1_00 / total:.2f}")







'''simple docstring'''

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"						,	[False, True]      )
def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_						,	keep_in_memory=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize(
    "features"						,	[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]						,	)
def   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	features=UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     str      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_						,	split=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )
			assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"						,	[str, list]      )
def   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     List[str]      )					->       Optional[int]:







			'''simple docstring'''



			if issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							csv_path
			elif issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							[csv_path]
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     List[str]=("train",)      )					->       Optional[int]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			for split in splits:
						lowerCAmelCase__								=							dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"						,	[False, True]      )
def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Optional[int]      )					->       Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_						,	keep_in_memory=UpperCamelCase_      ).read()
			_check_csv_datasetdict(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize(
    "features"						,	[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]						,	)
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Dict      )					->       Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	features=UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_datasetdict(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     List[Any]      )					->       Optional[int]:







			'''simple docstring'''



			if split:
						lowerCAmelCase__								=							{split: csv_path}
			else:
						lowerCAmelCase__								=							{"train": csv_path, "test": csv_path}
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_datasetdict(UpperCamelCase_						,	UpperCamelCase_						,	splits=list(path.keys()      )      )
			assert all(dataset[split].split == split for split in path.keys()      )







def   A     (      UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



			with open(UpperCamelCase_						,	encoding="utf-8"      ) as csvfile:
						yield from csv.reader(UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=1      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

			for rowa, rowa in zip(UpperCamelCase_						,	UpperCamelCase_      ):
						assert rowa == rowa







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Dict      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=2      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

			for rowa, rowa in zip(UpperCamelCase_						,	UpperCamelCase_      ):
						assert rowa == rowa







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			with pytest.raises(UpperCamelCase_      ):
						CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=0      )





