



import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


UpperCAmelCase							=       logging.get_logger(__name__)  # pylint: disable=invalid-name

def       A_   (						self  :					Dict			,		__a  :					List[str]			,		__a  :					Dict			,		__a  :					Tuple			,		__a  :					List[Any]		):

				"""simple docstring"""
				a__					   =		image.to(device=A__		)
				a__					   =		image  # take image as image_embeddings
				a__					   =		image_embeddings.unsqueeze(1		)

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				a__					   =		image_embeddings.shape
				a__					   =		image_embeddings.repeat(1			,		A__			,		1		)
				a__					   =		image_embeddings.view(bs_embed * num_images_per_prompt			,		A__			,		-1		)

				if do_classifier_free_guidance:
								a__					   =		torch.zeros_like(A__		)

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								a__					   =		torch.cat([uncond_embeddings, image_embeddings]		)

				return image_embeddings




class 			__snake_case   ( _lowerCamelCase):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_	,	a_	,	a_ = None	,	):
											super().__init__()

											a__					   =		{'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

											a__					   =		decoder_pipe_kwargs.get("""torch_dtype"""	,	__UpperCamelCase		) or prior.dtype

											a__					   =		StableDiffusionImageVariationPipeline.from_pretrained(
											    """lambdalabs/sd-image-variations-diffusers"""	,	**__UpperCamelCase		)

											# replace `_encode_image` method
											a__					   =		types.MethodType(_encode_image	,	self.decoder_pipe		)

											self.register_modules(
											    prior=__UpperCamelCase	,	tokenizer=__UpperCamelCase	,	text_encoder=__UpperCamelCase	,	prior_scheduler=__UpperCamelCase	,	)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	a_ = None	,	a_ = None	,	):







											if text_model_output is None:
															a__					   =		len(__UpperCamelCase		) if isinstance(__UpperCamelCase	,	__UpperCamelCase		) else 1
															# get prompt text embeddings
															a__					   =		self.tokenizer(
															    __UpperCamelCase	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	return_tensors="""pt"""	,	)
															a__					   =		text_inputs.input_ids
															a__					   =		text_inputs.attention_mask.bool().to(__UpperCamelCase		)

															if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			a__					   =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
																			logger.warning(
																			    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																			    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''		)
																			a__					   =		text_input_ids[:, : self.tokenizer.model_max_length]

															a__					   =		self.text_encoder(text_input_ids.to(__UpperCamelCase		)		)

															a__					   =		text_encoder_output.text_embeds
															a__					   =		text_encoder_output.last_hidden_state

											else:
															a__					   =		text_model_output[0].shape[0]
															a__					   =		text_model_output[0], text_model_output[1]
															a__					   =		text_attention_mask

											a__					   =		text_embeddings.repeat_interleave(__UpperCamelCase	,	dim=0		)
											a__					   =		text_encoder_hidden_states.repeat_interleave(__UpperCamelCase	,	dim=0		)
											a__					   =		text_mask.repeat_interleave(__UpperCamelCase	,	dim=0		)

											if do_classifier_free_guidance:
															a__					   =		[''] * batch_size

															a__					   =		self.tokenizer(
															    __UpperCamelCase	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=__UpperCamelCase	,	return_tensors="""pt"""	,	)
															a__					   =		uncond_input.attention_mask.bool().to(__UpperCamelCase		)
															a__					   =		self.text_encoder(uncond_input.input_ids.to(__UpperCamelCase		)		)

															a__					   =		uncond_embeddings_text_encoder_output.text_embeds
															a__					   =		uncond_embeddings_text_encoder_output.last_hidden_state

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

															a__					   =		uncond_embeddings.shape[1]
															a__					   =		uncond_embeddings.repeat(1	,	__UpperCamelCase		)
															a__					   =		uncond_embeddings.view(batch_size * num_images_per_prompt	,	__UpperCamelCase		)

															a__					   =		uncond_text_encoder_hidden_states.shape[1]
															a__					   =		uncond_text_encoder_hidden_states.repeat(1	,	__UpperCamelCase	,	1		)
															a__					   =		uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt	,	__UpperCamelCase	,	-1		)
															a__					   =		uncond_text_mask.repeat_interleave(__UpperCamelCase	,	dim=0		)

															# done duplicates

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															a__					   =		torch.cat([uncond_embeddings, text_embeddings]		)
															a__					   =		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

															a__					   =		torch.cat([uncond_text_mask, text_mask]		)

											return text_embeddings, text_encoder_hidden_states, text_mask


							@property
							def _a   (				self		):







											if self.device != torch.device("""meta"""		) or not hasattr(self.prior	,	"""_hf_hook"""		):
															return self.device
											for module in self.prior.modules():
															if (
															    hasattr(__UpperCamelCase	,	"""_hf_hook"""		)
															    and hasattr(module._hf_hook	,	"""execution_device"""		)
															    and module._hf_hook.execution_device is not None
															):
																			return torch.device(module._hf_hook.execution_device		)
											return self.device


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	a_	,	a_		):







											if latents is None:
															a__					   =		randn_tensor(__UpperCamelCase	,	generator=__UpperCamelCase	,	device=__UpperCamelCase	,	dtype=__UpperCamelCase		)
											else:
															if latents.shape != shape:
																			raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
															a__					   =		latents.to(__UpperCamelCase		)

											a__					   =		latents * scheduler.init_noise_sigma
											return latents


							def _a   (				self	,	a_ = None		):
											self.decoder_pipe.to(__UpperCamelCase		)
											super().to(__UpperCamelCase		)







							@torch.no_grad()
							def __call__(				self	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = 1	,	a_ = 25	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = 4.0	,	a_ = 8.0	,	a_ = 50	,	a_ = 1	,	a_ = 0.0	,	a_ = "pil"	,	a_ = True	,	):







											if prompt is not None:
															if isinstance(__UpperCamelCase	,	__UpperCamelCase		):
																			a__					   =		1
															elif isinstance(__UpperCamelCase	,	__UpperCamelCase		):
																			a__					   =		len(__UpperCamelCase		)
															else:
																			raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase		)}'''		)
											else:
															a__					   =		text_model_output[0].shape[0]

											a__					   =		self._execution_device

											a__					   =		batch_size * num_images_per_prompt

											a__					   =		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

											a__					   =		self._encode_prompt(
											    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase		)

											# prior

											self.prior_scheduler.set_timesteps(__UpperCamelCase	,	device=__UpperCamelCase		)
											a__					   =		self.prior_scheduler.timesteps

											a__					   =		self.prior.config.embedding_dim

											a__					   =		self.prepare_latents(
											    (batch_size, embedding_dim)	,	text_embeddings.dtype	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	self.prior_scheduler	,	)

											for i, t in enumerate(self.progress_bar(__UpperCamelCase		)		):
															# expand the latents if we are doing classifier free guidance
															a__					   =		torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents

															a__					   =		self.prior(
															    __UpperCamelCase	,	timestep=__UpperCamelCase	,	proj_embedding=__UpperCamelCase	,	encoder_hidden_states=__UpperCamelCase	,	attention_mask=__UpperCamelCase	,	).predicted_image_embedding

															if do_classifier_free_guidance:
																			a__					   =		predicted_image_embedding.chunk(2		)
																			a__					   =		predicted_image_embedding_uncond + prior_guidance_scale * (
																			    predicted_image_embedding_text - predicted_image_embedding_uncond
																			)

															if i + 1 == prior_timesteps_tensor.shape[0]:
																			a__					   =		None
															else:
																			a__					   =		prior_timesteps_tensor[i + 1]

															a__					   =		self.prior_scheduler.step(
															    __UpperCamelCase	,	timestep=__UpperCamelCase	,	sample=__UpperCamelCase	,	generator=__UpperCamelCase	,	prev_timestep=__UpperCamelCase	,	).prev_sample

											a__					   =		self.prior.post_process_latents(__UpperCamelCase		)

											a__					   =		prior_latents

											a__					   =		self.decoder_pipe(
											    image=__UpperCamelCase	,	height=__UpperCamelCase	,	width=__UpperCamelCase	,	num_inference_steps=__UpperCamelCase	,	guidance_scale=__UpperCamelCase	,	generator=__UpperCamelCase	,	output_type=__UpperCamelCase	,	return_dict=__UpperCamelCase	,	num_images_per_prompt=__UpperCamelCase	,	eta=__UpperCamelCase	,	)
											return output


import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 					__snake_case    (     _lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  RealmTokenizer
					__lowerCamelCase			   =  RealmTokenizerFast
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  filter_non_english




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									super().setUp()

									snake_case__   :      Any   				= [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									snake_case__   :      int   				= os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES['vocab_file']						)
									with open(self.vocab_file       ,			'w'       ,			encoding='utf-8'						) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= 'UNwant\u00E9d,running'
									snake_case__   :      Optional[Any]   				= 'unwanted, running'
									return input_text, output_text




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= self.tokenizer_class(self.vocab_file						)

									snake_case__   :      Any   				= tokenizer.tokenize('UNwant\u00E9d,running'						)
									self.assertListEqual(__UpperCamelCase       ,			['un', '##want', '##ed', ',', 'runn', '##ing']						)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase						)       ,			[9, 6, 7, 12, 10, 11]						)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									if not self.test_rust_tokenizer:
													return

									snake_case__   :      Any   				= self.get_tokenizer()
									snake_case__   :      Tuple   				= self.get_rust_tokenizer()

									snake_case__   :      Tuple   				= 'UNwant\u00E9d,running'

									snake_case__   :      Optional[int]   				= tokenizer.tokenize(__UpperCamelCase						)
									snake_case__   :      Dict   				= rust_tokenizer.tokenize(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Optional[int]   				= tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									snake_case__   :      int   				= rust_tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Optional[int]   				= self.get_rust_tokenizer()
									snake_case__   :      Optional[Any]   				= tokenizer.encode(__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= rust_tokenizer.encode(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									# With lower casing
									snake_case__   :      Union[str, Any]   				= self.get_tokenizer(do_lower_case=__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= self.get_rust_tokenizer(do_lower_case=__UpperCamelCase						)

									snake_case__   :      str   				= 'UNwant\u00E9d,running'

									snake_case__   :      List[Any]   				= tokenizer.tokenize(__UpperCamelCase						)
									snake_case__   :      str   				= rust_tokenizer.tokenize(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									snake_case__   :      str   				= rust_tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Any   				= self.get_rust_tokenizer()
									snake_case__   :      int   				= tokenizer.encode(__UpperCamelCase						)
									snake_case__   :      List[str]   				= rust_tokenizer.encode(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'						)       ,			['ah', '\u535A', '\u63A8', 'zz']						)




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Any   				= BasicTokenizer(do_lower_case=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)       ,			['hello', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['hello']						)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['hällo', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['h\u00E9llo']						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      int   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['hallo', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['hello']						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Any   				= BasicTokenizer(do_lower_case=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['hallo', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['hello']						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= BasicTokenizer(do_lower_case=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)       ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?']						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['HäLLo', '!', 'how', 'Are', 'yoU', '?']						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Dict   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['HaLLo', '!', 'how', 'Are', 'yoU', '?']						)




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			never_split=['[UNK]']						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'						)       ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']						)




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      List[str]   				= ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

									snake_case__   :      List[str]   				= {}
									for i, token in enumerate(__UpperCamelCase						):
													snake_case__   :      Union[str, Any]   				= i
									snake_case__   :      List[Any]   				= WordpieceTokenizer(vocab=__UpperCamelCase       ,			unk_token='[UNK]'						)

									self.assertListEqual(tokenizer.tokenize(''						)       ,			[]						)

									self.assertListEqual(tokenizer.tokenize('unwanted running'						)       ,			['un', '##want', '##ed', 'runn', '##ing']						)

									self.assertListEqual(tokenizer.tokenize('unwantedX running'						)       ,			['[UNK]', 'runn', '##ing']						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									self.assertTrue(_is_whitespace(' '						)						)
									self.assertTrue(_is_whitespace('\t'						)						)
									self.assertTrue(_is_whitespace('\r'						)						)
									self.assertTrue(_is_whitespace('\n'						)						)
									self.assertTrue(_is_whitespace('\u00A0'						)						)

									self.assertFalse(_is_whitespace('A'						)						)
									self.assertFalse(_is_whitespace('-'						)						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									self.assertTrue(_is_control('\u0005'						)						)

									self.assertFalse(_is_control('A'						)						)
									self.assertFalse(_is_control(' '						)						)
									self.assertFalse(_is_control('\t'						)						)
									self.assertFalse(_is_control('\r'						)						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									self.assertTrue(_is_punctuation('-'						)						)
									self.assertTrue(_is_punctuation('$'						)						)
									self.assertTrue(_is_punctuation('`'						)						)
									self.assertTrue(_is_punctuation('.'						)						)

									self.assertFalse(_is_punctuation('A'						)						)
									self.assertFalse(_is_punctuation(' '						)						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.get_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(__UpperCamelCase						) for t in ['Test', '\xad', 'test']]       ,			[['[UNK]'], [], ['[UNK]']]						)

									if self.test_rust_tokenizer:
													snake_case__   :      int   				= self.get_rust_tokenizer()
													self.assertListEqual(
													    [rust_tokenizer.tokenize(__UpperCamelCase						) for t in ['Test', '\xad', 'test']]       ,			[['[UNK]'], [], ['[UNK]']]						)




					@slow
					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Any   				= self.tokenizer_class.from_pretrained('bert-base-uncased'						)

									snake_case__   :      Optional[int]   				= tokenizer.encode('sequence builders'       ,			add_special_tokens=__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= tokenizer.encode('multi-sequence build'       ,			add_special_tokens=__UpperCamelCase						)

									snake_case__   :      str   				= tokenizer.build_inputs_with_special_tokens(__UpperCamelCase						)
									snake_case__   :      Tuple   				= tokenizer.build_inputs_with_special_tokens(__UpperCamelCase       ,			__UpperCamelCase						)

									assert encoded_sentence == [101] + text + [102]
									assert encoded_pair == [101] + text + [102] + text_a + [102]




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																	snake_case__   :      Union[str, Any]   				= self.rust_tokenizer_class.from_pretrained(__UpperCamelCase       ,			**__UpperCamelCase						)

																	snake_case__   :      int   				= F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																	snake_case__   :      Optional[Any]   				= tokenizer_r.encode_plus(
																	    __UpperCamelCase       ,			return_attention_mask=__UpperCamelCase       ,			return_token_type_ids=__UpperCamelCase       ,			return_offsets_mapping=__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase       ,			)

																	snake_case__   :      List[str]   				= tokenizer_r.do_lower_case if hasattr(__UpperCamelCase       ,			'do_lower_case'						) else False
																	snake_case__   :      Union[str, Any]   				= (
																	    [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'A'),
																	        ((1, 2), ','),
																	        ((3, 5), 'na'),
																	        ((5, 6), '##ï'),
																	        ((6, 8), '##ve'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'Allen'),
																	        ((21, 23), '##NL'),
																	        ((23, 24), '##P'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	    if not do_lower_case
																	    else [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'a'),
																	        ((1, 2), ','),
																	        ((3, 8), 'naive'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'allen'),
																	        ((21, 23), '##nl'),
																	        ((23, 24), '##p'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	)

																	self.assertEqual(
																	    [e[1] for e in expected_results]       ,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids']						)						)
																	self.assertEqual([e[0] for e in expected_results]       ,			tokens['offset_mapping']						)




					@slow
					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																	snake_case__   :      Dict   				= self.rust_tokenizer_class.from_pretrained(__UpperCamelCase       ,			**__UpperCamelCase						)
																	snake_case__   :      Optional[Any]   				= self.tokenizer_class.from_pretrained(__UpperCamelCase       ,			**__UpperCamelCase						)
																	snake_case__   :      int   				= [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

																	snake_case__   :      Optional[int]   				= tokenizer_r.batch_encode_candidates(__UpperCamelCase       ,			max_length=10       ,			return_tensors='np'						)
																	snake_case__   :      Tuple   				= tokenizer_p.batch_encode_candidates(__UpperCamelCase       ,			max_length=10       ,			return_tensors='np'						)

																	snake_case__   :      Optional[int]   				= (2, 2, 10)

																	self.assertEqual(encoded_sentence_r['input_ids'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_r['attention_mask'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_r['token_type_ids'].shape       ,			__UpperCamelCase						)

																	self.assertEqual(encoded_sentence_p['input_ids'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_p['attention_mask'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_p['token_type_ids'].shape       ,			__UpperCamelCase						)


