



import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
							import jax.numpy as jnp


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __get__(     self					:       str				,					_snake_case					:       List[str]				,					_snake_case					:       List[Any]=None					):






								"""simple docstring"""


								if obj is None:
											return self
								if self.fget is None:
											raise AttributeError('unreadable attribute'					)
								A__       =    '__cached_' + self.fget.__name__
								A__       =    getattr(_snake_case				,					_snake_case				,					_snake_case					)
								if cached is None:
											A__       =    self.fget(_snake_case					)
											setattr(_snake_case				,					_snake_case				,					_snake_case					)
								return cached







def A						(       __UpperCamelCase					)						->      Optional[Any]:
			A__       =    val.lower()
			if val in {"y", "yes", "t", "true", "on", "1"}:
						return 1
			if val in {"n", "no", "f", "false", "off", "0"}:
						return 0
			raise ValueError(f'''invalid truth value {val!r}'''					)


def A						(       __UpperCamelCase					)						->      Any:
			if is_torch_fx_proxy(__UpperCamelCase					):
						return True
			if is_torch_available():
						import torch

						if isinstance(__UpperCamelCase ,	torch.Tensor					):
									return True
			if is_tf_available():
						import tensorflow as tf

						if isinstance(__UpperCamelCase ,	tf.Tensor					):
									return True

			if is_flax_available():
						import jax.numpy as jnp
						from jax.core import Tracer

						if isinstance(__UpperCamelCase ,	(jnp.ndarray, Tracer)					):
									return True

			return isinstance(__UpperCamelCase ,	np.ndarray					)


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			return isinstance(__UpperCamelCase ,	np.ndarray					)


def A						(       __UpperCamelCase					)						->      List[Any]:
			return _is_numpy(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      str:
			import torch

			return isinstance(__UpperCamelCase ,	torch.Tensor					)


def A						(       __UpperCamelCase					)						->      Any:
			return False if not is_torch_available() else _is_torch(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      str:
			import torch

			return isinstance(__UpperCamelCase ,	torch.device					)


def A						(       __UpperCamelCase					)						->      List[Any]:
			return False if not is_torch_available() else _is_torch_device(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      List[Any]:
			import torch

			if isinstance(__UpperCamelCase ,	__UpperCamelCase					):
						if hasattr(__UpperCamelCase ,	__UpperCamelCase					):
									A__       =    getattr(__UpperCamelCase ,	__UpperCamelCase					)
						else:
									return False
			return isinstance(__UpperCamelCase ,	torch.dtype					)


def A						(       __UpperCamelCase					)						->      List[Any]:
			return False if not is_torch_available() else _is_torch_dtype(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      Any:
			import tensorflow as tf

			return isinstance(__UpperCamelCase ,	tf.Tensor					)


def A						(       __UpperCamelCase					)						->      List[Any]:
			return False if not is_tf_available() else _is_tensorflow(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      Any:
			import tensorflow as tf

			# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
			if hasattr(__UpperCamelCase ,	'is_symbolic_tensor'					):
						return tf.is_symbolic_tensor(__UpperCamelCase					)
			return type(__UpperCamelCase					) == tf.Tensor


def A						(       __UpperCamelCase					)						->      Any:
			return False if not is_tf_available() else _is_tf_symbolic_tensor(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      Optional[Any]:
			import jax.numpy as jnp  # noqa: F811

			return isinstance(__UpperCamelCase ,	jnp.ndarray					)


def A						(       __UpperCamelCase					)						->      str:
			return False if not is_flax_available() else _is_jax(__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      Any:
			if isinstance(__UpperCamelCase ,	(dict, UserDict)					):
						return {k: to_py_obj(__UpperCamelCase					) for k, v in obj.items()}
			elif isinstance(__UpperCamelCase ,	(list, tuple)					):
						return [to_py_obj(__UpperCamelCase					) for o in obj]
			elif is_tf_tensor(__UpperCamelCase					):
						return obj.numpy().tolist()
			elif is_torch_tensor(__UpperCamelCase					):
						return obj.detach().cpu().tolist()
			elif is_jax_tensor(__UpperCamelCase					):
						return np.asarray(__UpperCamelCase					).tolist()
			elif isinstance(__UpperCamelCase ,	(np.ndarray, np.number)					):  # tolist also works on 0d np arrays
						return obj.tolist()
			else:
						return obj


def A						(       __UpperCamelCase					)						->      Dict:
			if isinstance(__UpperCamelCase ,	(dict, UserDict)					):
						return {k: to_numpy(__UpperCamelCase					) for k, v in obj.items()}
			elif isinstance(__UpperCamelCase ,	(list, tuple)					):
						return np.array(__UpperCamelCase					)
			elif is_tf_tensor(__UpperCamelCase					):
						return obj.numpy()
			elif is_torch_tensor(__UpperCamelCase					):
						return obj.detach().cpu().numpy()
			elif is_jax_tensor(__UpperCamelCase					):
						return np.asarray(__UpperCamelCase					)
			else:
						return obj


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    fields(self					)

								# Safety and consistency checks
								if not len(_snake_case					):
											raise ValueError(F'''{self.__class__.__name__} has no fields.'''					)
								if not all(field.default is None for field in class_fields[1:]					):
											raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.'''					)

								A__       =    getattr(self				,					class_fields[0].name					)
								A__       =    all(getattr(self				,					field.name					) is None for field in class_fields[1:]					)

								if other_fields_are_none and not is_tensor(_snake_case					):
											if isinstance(_snake_case				,					_snake_case					):
														A__       =    first_field.items()
														A__       =    True
											else:
														try:
																	A__       =    iter(_snake_case					)
																	A__       =    True
														except TypeError:
																	A__       =    False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
											if first_field_iterator:
														for idx, element in enumerate(_snake_case					):
																	if (
																	    not isinstance(_snake_case				,					(list, tuple)					)
																	    or not len(_snake_case					) == 2
																	    or not isinstance(element[0]				,					_snake_case					)
																	):
																				if idx == 0:
																							# If we do not have an iterator of key/values, set it as attribute
																							A__       =    first_field
																				else:
																							# If we have a mixed iterator, raise an error
																							raise ValueError(
																							    F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).'''					)
																				break
																	setattr(self				,					element[0]				,					element[1]					)
																	if element[1] is not None:
																				A__       =    element[1]
											elif first_field is not None:
														A__       =    first_field
								else:
											for field in class_fields:
														A__       =    getattr(self				,					field.name					)
														if v is not None:
																	A__       =    v



					def __delitem__(     self					:       List[Any]				,					*_snake_case					:       str				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''					)



					def     _a							(     self					:       Any				,					*_snake_case					:       List[str]				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''					)



					def     _a							(     self					:       Optional[int]				,					*_snake_case					:       List[Any]				,					**_snake_case					:       str					):






								"""simple docstring"""


								raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''					)



					def     _a							(     self					:       Union[str, Any]				,					*_snake_case					:       Tuple				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''					)



					def __getitem__(     self					:       Optional[int]				,					_snake_case					:       List[Any]					):






								"""simple docstring"""


								if isinstance(_snake_case				,					_snake_case					):
											A__       =    dict(self.items()					)
											return inner_dict[k]
								else:
											return self.to_tuple()[k]



					def __setattr__(     self					:       Union[str, Any]				,					_snake_case					:       Tuple				,					_snake_case					:       int					):






								"""simple docstring"""


								if name in self.keys() and value is not None:
											# Don't call self.__setitem__ to avoid recursion errors
											super().__setitem__(_snake_case				,					_snake_case					)
								super().__setattr__(_snake_case				,					_snake_case					)



					def __setitem__(     self					:       List[str]				,					_snake_case					:       str				,					_snake_case					:       Any					):






								"""simple docstring"""


								super().__setitem__(_snake_case				,					_snake_case					)
								# Don't call self.__setattr__ to avoid recursion errors
								super().__setattr__(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								return tuple(self[k] for k in self.keys()					)







class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_      ):
					"""simple docstring"""






					@classmethod
					def     _a							(     cls					:       Any				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								raise ValueError(
								    F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()					)}'''					)







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Optional[Any]			       =    "longest"
					A__				:		str			       =    "max_length"
					A__				:		Dict			       =    "do_not_pad"







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		int			       =    "pt"
					A__				:		List[str]			       =    "tf"
					A__				:		List[Any]			       =    "np"
					A__				:		Any			       =    "jax"







class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Optional[int]				,					_snake_case					:       List[ContextManager]					):






								"""simple docstring"""


								A__       =    context_managers
								A__       =    ExitStack()



					def __enter__(     self					:       Tuple					):






								"""simple docstring"""


								for context_manager in self.context_managers:
											self.stack.enter_context(_snake_case					)



					def __exit__(     self					:       List[Any]				,					*_snake_case					:       Tuple				,					**_snake_case					:       str					):






								"""simple docstring"""


								self.stack.__exit__(*_snake_case				,					**_snake_case					)







def A						(       __UpperCamelCase					)						->      List[Any]:
			A__       =    infer_framework(__UpperCamelCase					)
			if framework == "tf":
						A__       =    inspect.signature(model_class.call					)  # TensorFlow models
			elif framework == "pt":
						A__       =    inspect.signature(model_class.forward					)  # PyTorch models
			else:
						A__       =    inspect.signature(model_class.__call__					)  # Flax models

			for p in signature.parameters:
						if p == "return_loss" and signature.parameters[p].default is True:
									return True

			return False


def A						(       __UpperCamelCase					)						->      Dict:
			A__       =    model_class.__name__
			A__       =    infer_framework(__UpperCamelCase					)
			if framework == "tf":
						A__       =    inspect.signature(model_class.call					)  # TensorFlow models
			elif framework == "pt":
						A__       =    inspect.signature(model_class.forward					)  # PyTorch models
			else:
						A__       =    inspect.signature(model_class.__call__					)  # Flax models

			if "QuestionAnswering" in model_name:
						return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
			else:
						return [p for p in signature.parameters if "label" in p]


def A						(       __UpperCamelCase ,	__UpperCamelCase = "" ,	__UpperCamelCase = "."					)						->      Any:

			def _flatten_dict(__UpperCamelCase ,	__UpperCamelCase="" ,	__UpperCamelCase="."					):
						for k, v in d.items():
									A__       =    str(__UpperCamelCase					) + delimiter + str(__UpperCamelCase					) if parent_key else k
									if v and isinstance(__UpperCamelCase ,	__UpperCamelCase					):
												yield from flatten_dict(__UpperCamelCase ,	__UpperCamelCase ,	delimiter=__UpperCamelCase					).items()
									else:
												yield key, v

			return dict(_flatten_dict(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)					)


@contextmanager
def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      int:
			if use_temp_dir:
						with tempfile.TemporaryDirectory() as tmp_dir:
									yield tmp_dir
			else:
						yield working_dir


def A						(       __UpperCamelCase ,	__UpperCamelCase=None					)						->      List[Any]:
			if is_numpy_array(__UpperCamelCase					):
						return np.transpose(__UpperCamelCase ,	axes=__UpperCamelCase					)
			elif is_torch_tensor(__UpperCamelCase					):
						return array.T if axes is None else array.permute(*__UpperCamelCase					)
			elif is_tf_tensor(__UpperCamelCase					):
						import tensorflow as tf

						return tf.transpose(__UpperCamelCase ,	perm=__UpperCamelCase					)
			elif is_jax_tensor(__UpperCamelCase					):
						return jnp.transpose(__UpperCamelCase ,	axes=__UpperCamelCase					)
			else:
						raise ValueError(f'''Type not supported for transpose: {type(__UpperCamelCase					)}.'''					)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			if is_numpy_array(__UpperCamelCase					):
						return np.reshape(__UpperCamelCase ,	__UpperCamelCase					)
			elif is_torch_tensor(__UpperCamelCase					):
						return array.reshape(*__UpperCamelCase					)
			elif is_tf_tensor(__UpperCamelCase					):
						import tensorflow as tf

						return tf.reshape(__UpperCamelCase ,	__UpperCamelCase					)
			elif is_jax_tensor(__UpperCamelCase					):
						return jnp.reshape(__UpperCamelCase ,	__UpperCamelCase					)
			else:
						raise ValueError(f'''Type not supported for reshape: {type(__UpperCamelCase					)}.'''					)


def A						(       __UpperCamelCase ,	__UpperCamelCase=None					)						->      Tuple:
			if is_numpy_array(__UpperCamelCase					):
						return np.squeeze(__UpperCamelCase ,	axis=__UpperCamelCase					)
			elif is_torch_tensor(__UpperCamelCase					):
						return array.squeeze() if axis is None else array.squeeze(dim=__UpperCamelCase					)
			elif is_tf_tensor(__UpperCamelCase					):
						import tensorflow as tf

						return tf.squeeze(__UpperCamelCase ,	axis=__UpperCamelCase					)
			elif is_jax_tensor(__UpperCamelCase					):
						return jnp.squeeze(__UpperCamelCase ,	axis=__UpperCamelCase					)
			else:
						raise ValueError(f'''Type not supported for squeeze: {type(__UpperCamelCase					)}.'''					)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			if is_numpy_array(__UpperCamelCase					):
						return np.expand_dims(__UpperCamelCase ,	__UpperCamelCase					)
			elif is_torch_tensor(__UpperCamelCase					):
						return array.unsqueeze(dim=__UpperCamelCase					)
			elif is_tf_tensor(__UpperCamelCase					):
						import tensorflow as tf

						return tf.expand_dims(__UpperCamelCase ,	axis=__UpperCamelCase					)
			elif is_jax_tensor(__UpperCamelCase					):
						return jnp.expand_dims(__UpperCamelCase ,	axis=__UpperCamelCase					)
			else:
						raise ValueError(f'''Type not supported for expand_dims: {type(__UpperCamelCase					)}.'''					)


def A						(       __UpperCamelCase					)						->      Optional[Any]:
			if is_numpy_array(__UpperCamelCase					):
						return np.size(__UpperCamelCase					)
			elif is_torch_tensor(__UpperCamelCase					):
						return array.numel()
			elif is_tf_tensor(__UpperCamelCase					):
						import tensorflow as tf

						return tf.size(__UpperCamelCase					)
			elif is_jax_tensor(__UpperCamelCase					):
						return array.size
			else:
						raise ValueError(f'''Type not supported for expand_dims: {type(__UpperCamelCase					)}.'''					)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			for key, value in auto_map.items():
						if isinstance(__UpperCamelCase ,	(tuple, list)					):
									A__       =    [f'''{repo_id}--{v}''' if (v is not None and '--' not in v) else v for v in value]
						elif value is not None and "--" not in value:
									A__       =    f'''{repo_id}--{value}'''

			return auto_map






def A						(       __UpperCamelCase					)						->      Optional[int]:
			for base_class in inspect.getmro(__UpperCamelCase					):
						A__       =    base_class.__module__
						A__       =    base_class.__name__
						if module.startswith('tensorflow'					) or module.startswith('keras'					) or name == "TFPreTrainedModel":
									return "tf"
						elif module.startswith('torch'					) or name == "PreTrainedModel":
									return "pt"
						elif module.startswith('flax'					) or module.startswith('jax'					) or name == "FlaxPreTrainedModel":
									return "flax"
			else:
						raise TypeError(f'''Could not infer framework from class {model_class}.'''					)









import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		int			       =    LongformerTokenizer
					A__				:		Optional[int]			       =    True
					A__				:		Any			       =    LongformerTokenizerFast
					A__				:		Dict			       =    True



					def     _a							(     self					:       int					):






								"""simple docstring"""


								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A__       =    [
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    '\u0120',
								    '\u0120l',
								    '\u0120n',
								    '\u0120lo',
								    '\u0120low',
								    'er',
								    '\u0120lowest',
								    '\u0120newer',
								    '\u0120wider',
								    '<unk>',
								]
								A__       =    dict(zip(_snake_case				,					range(len(_snake_case					)					)					)					)
								A__       =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
								A__       =    {'unk_token': '<unk>'}

								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['vocab_file']					)
								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['merges_file']					)
								with open(self.vocab_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write(json.dumps(_snake_case					) + '\n'					)
								with open(self.merges_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write('\n'.join(_snake_case					)					)



					def     _a							(     self					:       int				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Optional[int]				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Any				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    'lower newer'
								A__       =    'lower newer'
								return input_text, output_text



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.tokenizer_class(self.vocab_file				,					self.merges_file				,					**self.special_tokens_map					)
								A__       =    'lower newer'
								A__       =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
								A__       =    tokenizer.tokenize(_snake_case					)  # , add_prefix_space=True)
								self.assertListEqual(_snake_case				,					_snake_case					)

								A__       =    tokens + [tokenizer.unk_token]
								A__       =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					)				,					_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('Hello world!'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 2]					)
								self.assertListEqual(
								    tokenizer.encode('Hello world! cécé herlolip 418'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,					)



					@slow
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

								A__       =    tokenizer.encode('sequence builders'				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.encode('multi-sequence build'				,					add_special_tokens=_snake_case					)

								A__       =    tokenizer.encode(
								    'sequence builders'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.encode(
								    'sequence builders'				,					'multi-sequence build'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)

								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case					)
								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case				,					_snake_case					)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								A__       =    'Encode this sequence.'
								A__       =    tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

								# Testing encoder arguments
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								tokenizer.add_special_tokens({'bos_token': '<s>'}					)
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								# Testing spaces after special tokens
								A__       =    '<mask>'
								tokenizer.add_special_tokens(
								    {'mask_token': AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					)}					)  # mask token has a left space
								A__       =    tokenizer.convert_tokens_to_ids(_snake_case					)

								A__       =    'Encode <mask> sequence'
								A__       =    'Encode <mask>sequence'

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    self.rust_tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    self.tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    'A, <mask> AllenNLP sentence.'
														A__       =    tokenizer_r.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)
														A__       =    tokenizer_p.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['token_type_ids']					)				,					sum(tokens_p['token_type_ids']					)					)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)				,					sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)				,					)

														A__       =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
														A__       =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
														self.assertSequenceEqual(tokens_r['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for trim_offsets, add_prefix_space in itertools.product([True, False]				,					repeat=2					):
											A__       =    self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)

											A__       =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											A__       =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['add_prefix_space']				,					_snake_case					)

											self.assertEqual(post_processor_state['add_prefix_space']				,					_snake_case					)
											self.assertEqual(post_processor_state['trim_offsets']				,					_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
														A__       =    F'''{text_of_1_token} {text_of_1_token}'''

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    F''' {text}'''

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(1, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					) + 1, 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)




