

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a		=logging.get_logger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Any						      =      ['''pixel_values''']
							def __init__(		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Union[int, float] = 1 / 2_5_5   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,**SCREAMING_SNAKE_CASE__		:			int   ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = size if size is not None else {'shortest_edge': 2_2_4}
								__lowerCamelCase				:				List[Any]					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowerCamelCase				:				str					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,param_name='crop_size')

								__lowerCamelCase				:				Any					       = do_resize
								__lowerCamelCase				:				Optional[Any]					       = size
								__lowerCamelCase				:				List[Any]					       = resample
								__lowerCamelCase				:				Optional[int]					       = do_center_crop
								__lowerCamelCase				:				Optional[int]					       = crop_size
								__lowerCamelCase				:				List[str]					       = do_rescale
								__lowerCamelCase				:				Optional[Any]					       = rescale_factor
								__lowerCamelCase				:				Dict					       = do_normalize
								__lowerCamelCase				:				int					       = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								__lowerCamelCase				:				Union[str, Any]					       = image_std if image_std is not None else IMAGENET_DEFAULT_STD
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[Any]   ,):
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
									__lowerCamelCase				:				List[str]					       = int((2_5_6 / 2_2_4) * size['shortest_edge'])
									__lowerCamelCase				:				Union[str, Any]					       = get_resize_output_image_size(SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				str					       = {'height': output_size[0], 'width': output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}")
								return resize(
								    SCREAMING_SNAKE_CASE__   ,size=(size_dict['height'], size_dict['width'])   ,resample=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[str]   ,):
								__lowerCamelCase				:				List[Any]					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								if "height" not in size or "width" not in size:
									raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}")
								return center_crop(SCREAMING_SNAKE_CASE__   ,size=(size['height'], size['width'])   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[int, float]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Dict   ,):
								return rescale(SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,):
								return normalize(SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			ImageInput   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Dict[str, int]] = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Dict[str, int]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[float] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[TensorType] = None   ,SCREAMING_SNAKE_CASE__		:			ChannelDimension = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,):
								__lowerCamelCase				:				Union[str, Any]					       = do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase				:				Tuple					       = resample if resample is not None else self.resample
								__lowerCamelCase				:				str					       = do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase				:				Union[str, Any]					       = do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase				:				Any					       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase				:				Union[str, Any]					       = do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase				:				Dict					       = image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase				:				Dict					       = image_std if image_std is not None else self.image_std

								__lowerCamelCase				:				List[Any]					       = size if size is not None else self.size
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,param_name='crop_size')

								__lowerCamelCase				:				Optional[Any]					       = make_list_of_images(SCREAMING_SNAKE_CASE__)

								if not valid_images(SCREAMING_SNAKE_CASE__):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.')

								if do_resize and size is None:
									raise ValueError('Size must be specified if do_resize is True.')

								if do_center_crop and crop_size is None:
									raise ValueError('Crop size must be specified if do_center_crop is True.')

								if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.')

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.')

								# All transformations expect numpy arrays.
								__lowerCamelCase				:				str					       = [to_numpy_array(SCREAMING_SNAKE_CASE__) for image in images]

								if do_resize:
									__lowerCamelCase				:				Any					       = [self.resize(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								if do_center_crop:
									__lowerCamelCase				:				Union[str, Any]					       = [self.center_crop(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								if do_rescale:
									__lowerCamelCase				:				Any					       = [self.rescale(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase				:				List[Any]					       = [self.normalize(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Optional[Any]					       = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Union[str, Any]					       = {'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__   ,tensor_type=SCREAMING_SNAKE_CASE__)









'''simple docstring'''




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 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = LongformerTokenizer
	lowercase          = True
	lowercase          = LongformerTokenizerFast
	lowercase          = True





	def  _lowercase(  self    )			->			List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase     :		List[str]							     =		[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								UpperCAmelCase     :		int							     =		dict(zip(A		,		range(len(A    )    )    )    )
								UpperCAmelCase     :		Any							     =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								UpperCAmelCase     :		Dict							     =		{"""unk_token""": """<unk>"""}

								UpperCAmelCase     :		int							     =		os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES["""vocab_file"""]    )
								UpperCAmelCase     :		Optional[int]							     =		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(A    ) + """\n"""    )
								with open(self.merges_file		,		"""w"""		,		encoding="""utf-8"""    ) as fp:
															fp.write("""\n""".join(A    )    )





	def  _lowercase(  self		,		**A    )			->			Optional[Any]:
								kwargs.update(self.special_tokens_map    )
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		**A    )			->			int:
								kwargs.update(self.special_tokens_map    )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		"""lower newer"""
								UpperCAmelCase     :		Optional[int]							     =		"""lower newer"""
								return input_text, output_text





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Tuple							     =		self.tokenizer_class(self.vocab_file		,		self.merges_file		,		**self.special_tokens_map    )
								UpperCAmelCase     :		Dict							     =		"""lower newer"""
								UpperCAmelCase     :		int							     =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								UpperCAmelCase     :		Tuple							     =		tokenizer.tokenize(A    )  # , add_prefix_space=True)
								self.assertListEqual(A		,		A    )

								UpperCAmelCase     :		Any							     =		tokens + [tokenizer.unk_token]
								UpperCAmelCase     :		Tuple							     =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )		,		A    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								self.assertListEqual(tokenizer.encode("""Hello world!"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 2]    )
								self.assertListEqual(
								    tokenizer.encode("""Hello world! cécé herlolip 418"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,		)





	@slow
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Any							     =		self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""    )

								UpperCAmelCase     :		List[Any]							     =		tokenizer.encode("""sequence builders"""		,		add_special_tokens=A    )
								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.encode("""multi-sequence build"""		,		add_special_tokens=A    )

								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		"""multi-sequence build"""		,		add_special_tokens=A		,		add_prefix_space=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.build_inputs_with_special_tokens(A    )
								UpperCAmelCase     :		Any							     =		tokenizer.build_inputs_with_special_tokens(A		,		A    )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								UpperCAmelCase     :		List[Any]							     =		"""Encode this sequence."""
								UpperCAmelCase     :		List[str]							     =		tokenizer.byte_encoder[""" """.encode("""utf-8"""    )[0]]

								# Testing encoder arguments
								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		Dict							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertNotEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		int							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertEqual(A		,		A    )

								tokenizer.add_special_tokens({"""bos_token""": """<s>"""}    )
								UpperCAmelCase     :		int							     =		tokenizer.encode(A		,		add_special_tokens=A    )
								UpperCAmelCase     :		List[Any]							     =		tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
								self.assertNotEqual(A		,		A    )

								# Testing spaces after special tokens
								UpperCAmelCase     :		Union[str, Any]							     =		"""<mask>"""
								tokenizer.add_special_tokens(
								    {"""mask_token""": AddedToken(A		,		lstrip=A		,		rstrip=A    )}    )  # mask token has a left space
								UpperCAmelCase     :		str							     =		tokenizer.convert_tokens_to_ids(A    )

								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask> sequence"""
								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask>sequence"""

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		encoded.index(A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Optional[int]							     =		encoded.index(A    )
								UpperCAmelCase     :		Optional[int]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertNotEqual(A		,		A    )





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	def  _lowercase(  self    )			->			Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		str							     =		self.rust_tokenizer_class.from_pretrained(A		,		**A    )
																						UpperCAmelCase     :		int							     =		self.tokenizer_class.from_pretrained(A		,		**A    )
																						UpperCAmelCase     :		Dict							     =		"""A, <mask> AllenNLP sentence."""
																						UpperCAmelCase     :		Dict							     =		tokenizer_r.encode_plus(A		,		add_special_tokens=A		,		return_token_type_ids=A    )
																						UpperCAmelCase     :		Tuple							     =		tokenizer_p.encode_plus(A		,		add_special_tokens=A		,		return_token_type_ids=A    )

																						# 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"""]    )		,		)

																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																						UpperCAmelCase     :		int							     =		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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )
																						self.assertSequenceEqual(tokens_r["""input_ids"""]		,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )

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





	def  _lowercase(  self    )			->			List[Any]:
								for trim_offsets, add_prefix_space in itertools.product([True, False]		,		repeat=2    ):
															UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
															    self.tmpdirname		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )

															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

															self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,		A    )

															self.assertEqual(post_processor_state["""add_prefix_space"""]		,		A    )
															self.assertEqual(post_processor_state["""trim_offsets"""]		,		A    )






	def  _lowercase(  self    )			->			Optional[Any]:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		Union[str, Any]							     =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																						UpperCAmelCase     :		int							     =		f'''{text_of_1_token} {text_of_1_token}'''

																						UpperCAmelCase     :		List[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Dict							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		int							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		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)),
																						# )

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(1, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ) + 1, 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Union[str, Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Union[str, Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Optional[int]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

