



'''simple docstring'''




from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = ['vqvae']





	def __init__(  self		,		A		,		A		,		A		,		A		,		)			->			List[str]:
								super().__init__()
								self.register_modules(unet=A		,		scheduler=A		,		mel=A		,		vqvae=A    )





	def  _lowercase(  self    )			->			int:
								return 50 if isinstance(self.scheduler		,		A    ) else 1000





	@torch.no_grad()
	def __call__(  self		,		A = 1		,		A = None		,		A = None		,		A = 0		,		A = 0		,		A = None		,		A = None		,		A = 0		,		A = 0		,		A = None		,		A = 0		,		A = None		,		A = None		,		A=True		,		)			->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
								UpperCAmelCase     :		Dict							     =		steps or self.get_default_steps()
								self.scheduler.set_timesteps(A    )
								UpperCAmelCase     :		Optional[int]							     =		step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size    ) == int:
															UpperCAmelCase     :		Union[str, Any]							     =		(self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
															UpperCAmelCase     :		str							     =		randn_tensor(
															    (
															        batch_size,
															        self.unet.config.in_channels,
															        self.unet.config.sample_size[0],
															        self.unet.config.sample_size[1],
															    )		,		generator=A		,		device=self.device		,		)
								UpperCAmelCase     :		Dict							     =		noise
								UpperCAmelCase     :		Any							     =		None

								if audio_file is not None or raw_audio is not None:
															self.mel.load_audio(A		,		A    )
															UpperCAmelCase     :		int							     =		self.mel.audio_slice_to_image(A    )
															UpperCAmelCase     :		Optional[Any]							     =		np.frombuffer(input_image.tobytes()		,		dtype="""uint8"""    ).reshape(
															    (input_image.height, input_image.width)    )
															UpperCAmelCase     :		Union[str, Any]							     =		(input_image / 255) * 2 - 1
															UpperCAmelCase     :		Optional[int]							     =		torch.tensor(input_image[np.newaxis, :, :]		,		dtype=torch.float    ).to(self.device    )

															if self.vqvae is not None:
																						UpperCAmelCase     :		Optional[int]							     =		self.vqvae.encode(torch.unsqueeze(A		,		0    )    ).latent_dist.sample(
																						    generator=A    )[0]
																						UpperCAmelCase     :		Union[str, Any]							     =		self.vqvae.config.scaling_factor * input_images

															if start_step > 0:
																						UpperCAmelCase     :		Optional[Any]							     =		self.scheduler.add_noise(A		,		A		,		self.scheduler.timesteps[start_step - 1]    )

															UpperCAmelCase     :		Tuple							     =		(
															    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
															)
															UpperCAmelCase     :		int							     =		int(mask_start_secs * pixels_per_second    )
															UpperCAmelCase     :		List[Any]							     =		int(mask_end_secs * pixels_per_second    )
															UpperCAmelCase     :		List[str]							     =		self.scheduler.add_noise(A		,		A		,		torch.tensor(self.scheduler.timesteps[start_step:]    )    )

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
															if isinstance(self.unet		,		A    ):
																						UpperCAmelCase     :		Dict							     =		self.unet(A		,		A		,		A    )["""sample"""]
															else:
																						UpperCAmelCase     :		str							     =		self.unet(A		,		A    )["""sample"""]

															if isinstance(self.scheduler		,		A    ):
																						UpperCAmelCase     :		Union[str, Any]							     =		self.scheduler.step(
																						    model_output=A		,		timestep=A		,		sample=A		,		eta=A		,		generator=A		,		)["""prev_sample"""]
															else:
																						UpperCAmelCase     :		Any							     =		self.scheduler.step(
																						    model_output=A		,		timestep=A		,		sample=A		,		generator=A		,		)["""prev_sample"""]

															if mask is not None:
																						if mask_start > 0:
																													UpperCAmelCase     :		List[str]							     =		mask[:, step, :, :mask_start]
																						if mask_end > 0:
																													UpperCAmelCase     :		Optional[Any]							     =		mask[:, step, :, -mask_end:]

								if self.vqvae is not None:
															# 0.18215 was scaling factor used in training to ensure unit variance
															UpperCAmelCase     :		List[str]							     =		1 / self.vqvae.config.scaling_factor * images
															UpperCAmelCase     :		Union[str, Any]							     =		self.vqvae.decode(A    )["""sample"""]

								UpperCAmelCase     :		Any							     =		(images / 2 + 0.5).clamp(0		,		1    )
								UpperCAmelCase     :		Optional[int]							     =		images.cpu().permute(0		,		2		,		3		,		1    ).numpy()
								UpperCAmelCase     :		List[Any]							     =		(images * 255).round().astype("""uint8"""    )
								UpperCAmelCase     :		Union[str, Any]							     =		list(
								    (Image.fromarray(_[:, :, 0]    ) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(A		,		mode="""RGB"""    ).convert("""L"""    ) for _ in images)    )

								UpperCAmelCase     :		List[str]							     =		[self.mel.image_to_audio(A    ) for _ in images]
								if not return_dict:
															return images, (self.mel.get_sample_rate(), audios)

								return BaseOutput(**AudioPipelineOutput(np.array(A    )[:, np.newaxis, :]    )		,		**ImagePipelineOutput(A    )    )





	@torch.no_grad()
	def  _lowercase(  self		,		A		,		A = 50    )			->			np.ndarray:
								assert isinstance(self.scheduler		,		A    )
								self.scheduler.set_timesteps(A    )
								UpperCAmelCase     :		str							     =		np.array(
								    [np.frombuffer(image.tobytes()		,		dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
								UpperCAmelCase     :		Tuple							     =		(sample / 255) * 2 - 1
								UpperCAmelCase     :		Any							     =		torch.Tensor(A    ).to(self.device    )

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,		(0,)    )    ):
															UpperCAmelCase     :		int							     =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
															UpperCAmelCase     :		Any							     =		self.scheduler.alphas_cumprod[t]
															UpperCAmelCase     :		int							     =		(
															    self.scheduler.alphas_cumprod[prev_timestep]
															    if prev_timestep >= 0
															    else self.scheduler.final_alpha_cumprod
															)
															UpperCAmelCase     :		Any							     =		1 - alpha_prod_t
															UpperCAmelCase     :		Any							     =		self.unet(A		,		A    )["""sample"""]
															UpperCAmelCase     :		Union[str, Any]							     =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
															UpperCAmelCase     :		Optional[int]							     =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
															UpperCAmelCase     :		Union[str, Any]							     =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample






	@staticmethod
	def  _lowercase(  A		,		A		,		A    )			->			torch.Tensor:
								UpperCAmelCase     :		Tuple							     =		acos(torch.dot(torch.flatten(A    )		,		torch.flatten(A    )    ) / torch.norm(A    ) / torch.norm(A    )    )
								return sin((1 - alpha) * theta    ) * xa / sin(A    ) + sin(alpha * theta    ) * xa / sin(A    )






'''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    ))		,		)

