from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block





@dataclass
class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  42





class 					__snake_case    (     _lowerCamelCase       ,_lowerCamelCase      ):




					@register_to_config
					def __init__(  self       ,			__UpperCamelCase = None       ,			__UpperCamelCase = 3       ,			__UpperCamelCase = 3       ,			__UpperCamelCase = False       ,			__UpperCamelCase = "positional"       ,			__UpperCamelCase = 0       ,			__UpperCamelCase = True       ,			__UpperCamelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")       ,			__UpperCamelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")       ,			__UpperCamelCase = (224, 448, 672, 896)       ,			__UpperCamelCase = 2       ,			__UpperCamelCase = 1       ,			__UpperCamelCase = 1       ,			__UpperCamelCase = "conv"       ,			__UpperCamelCase = "conv"       ,			__UpperCamelCase = "silu"       ,			__UpperCamelCase = 8       ,			__UpperCamelCase = 32       ,			__UpperCamelCase = 1E-5       ,			__UpperCamelCase = "default"       ,			__UpperCamelCase = True       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			)	->    Optional[int]:



									'''simple docstring'''







									super().__init__()

									snake_case__   :      int   				= sample_size
									snake_case__   :      Any   				= block_out_channels[0] * 4

									# Check inputs
									if len(__UpperCamelCase						) != len(__UpperCamelCase						):
													raise ValueError(
													    F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""						)

									if len(__UpperCamelCase						) != len(__UpperCamelCase						):
													raise ValueError(
													    F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""						)

									# input
									snake_case__   :      List[str]   				= nn.Convad(__UpperCamelCase       ,			block_out_channels[0]       ,			kernel_size=3       ,			padding=(1, 1)						)

									# time
									if time_embedding_type == "fourier":
													snake_case__   :      Optional[int]   				= GaussianFourierProjection(embedding_size=block_out_channels[0]       ,			scale=16						)
													snake_case__   :      List[str]   				= 2 * block_out_channels[0]
									elif time_embedding_type == "positional":
													snake_case__   :      List[str]   				= Timesteps(block_out_channels[0]       ,			__UpperCamelCase       ,			__UpperCamelCase						)
													snake_case__   :      int   				= block_out_channels[0]

									snake_case__   :      Any   				= TimestepEmbedding(__UpperCamelCase       ,			__UpperCamelCase						)

									# class embedding
									if class_embed_type is None and num_class_embeds is not None:
													snake_case__   :      str   				= nn.Embedding(__UpperCamelCase       ,			__UpperCamelCase						)
									elif class_embed_type == "timestep":
													snake_case__   :      List[Any]   				= TimestepEmbedding(__UpperCamelCase       ,			__UpperCamelCase						)
									elif class_embed_type == "identity":
													snake_case__   :      Union[str, Any]   				= nn.Identity(__UpperCamelCase       ,			__UpperCamelCase						)
									else:
													snake_case__   :      str   				= None

									snake_case__   :      Union[str, Any]   				= nn.ModuleList([]						)
									snake_case__   :      str   				= None
									snake_case__   :      List[str]   				= nn.ModuleList([]						)

									# down
									snake_case__   :      str   				= block_out_channels[0]
									for i, down_block_type in enumerate(__UpperCamelCase						):
													snake_case__   :      Union[str, Any]   				= output_channel
													snake_case__   :      List[str]   				= block_out_channels[i]
													snake_case__   :      Union[str, Any]   				= i == len(__UpperCamelCase						) - 1

													snake_case__   :      Optional[int]   				= get_down_block(
													    __UpperCamelCase       ,			num_layers=__UpperCamelCase       ,			in_channels=__UpperCamelCase       ,			out_channels=__UpperCamelCase       ,			temb_channels=__UpperCamelCase       ,			add_downsample=not is_final_block       ,			resnet_eps=__UpperCamelCase       ,			resnet_act_fn=__UpperCamelCase       ,			resnet_groups=__UpperCamelCase       ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,			downsample_padding=__UpperCamelCase       ,			resnet_time_scale_shift=__UpperCamelCase       ,			downsample_type=__UpperCamelCase       ,			)
													self.down_blocks.append(__UpperCamelCase						)

									# mid
									snake_case__   :      Optional[Any]   				= UNetMidBlockaD(
									    in_channels=block_out_channels[-1]       ,			temb_channels=__UpperCamelCase       ,			resnet_eps=__UpperCamelCase       ,			resnet_act_fn=__UpperCamelCase       ,			output_scale_factor=__UpperCamelCase       ,			resnet_time_scale_shift=__UpperCamelCase       ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]       ,			resnet_groups=__UpperCamelCase       ,			add_attention=__UpperCamelCase       ,			)

									# up
									snake_case__   :      Dict   				= list(reversed(__UpperCamelCase						)						)
									snake_case__   :      Tuple   				= reversed_block_out_channels[0]
									for i, up_block_type in enumerate(__UpperCamelCase						):
													snake_case__   :      Union[str, Any]   				= output_channel
													snake_case__   :      Dict   				= reversed_block_out_channels[i]
													snake_case__   :      Union[str, Any]   				= reversed_block_out_channels[min(i + 1       ,			len(__UpperCamelCase						) - 1						)]

													snake_case__   :      Optional[Any]   				= i == len(__UpperCamelCase						) - 1

													snake_case__   :      Tuple   				= get_up_block(
													    __UpperCamelCase       ,			num_layers=layers_per_block + 1       ,			in_channels=__UpperCamelCase       ,			out_channels=__UpperCamelCase       ,			prev_output_channel=__UpperCamelCase       ,			temb_channels=__UpperCamelCase       ,			add_upsample=not is_final_block       ,			resnet_eps=__UpperCamelCase       ,			resnet_act_fn=__UpperCamelCase       ,			resnet_groups=__UpperCamelCase       ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,			resnet_time_scale_shift=__UpperCamelCase       ,			upsample_type=__UpperCamelCase       ,			)
													self.up_blocks.append(__UpperCamelCase						)
													snake_case__   :      List[str]   				= output_channel

									# out
									snake_case__   :      Tuple   				= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,			32						)
									snake_case__   :      Union[str, Any]   				= nn.GroupNorm(num_channels=block_out_channels[0]       ,			num_groups=__UpperCamelCase       ,			eps=__UpperCamelCase						)
									snake_case__   :      List[str]   				= nn.SiLU()
									snake_case__   :      Union[str, Any]   				= nn.Convad(block_out_channels[0]       ,			__UpperCamelCase       ,			kernel_size=3       ,			padding=1						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase = None       ,			__UpperCamelCase = True       ,			)	->    Union[UNetaDOutput, Tuple]:



									'''simple docstring'''







									if self.config.center_input_sample:
													snake_case__   :      str   				= 2 * sample - 1.0

									# 1. time
									snake_case__   :      Dict   				= timestep
									if not torch.is_tensor(__UpperCamelCase						):
													snake_case__   :      Dict   				= torch.tensor([timesteps]       ,			dtype=torch.long       ,			device=sample.device						)
									elif torch.is_tensor(__UpperCamelCase						) and len(timesteps.shape						) == 0:
													snake_case__   :      str   				= timesteps[None].to(sample.device						)

									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									snake_case__   :      Tuple   				= timesteps * torch.ones(sample.shape[0]       ,			dtype=timesteps.dtype       ,			device=timesteps.device						)

									snake_case__   :      Dict   				= self.time_proj(__UpperCamelCase						)

									# timesteps does not contain any weights and will always return f32 tensors
									# but time_embedding might actually be running in fp16. so we need to cast here.
									# there might be better ways to encapsulate this.
									snake_case__   :      int   				= t_emb.to(dtype=self.dtype						)
									snake_case__   :      Union[str, Any]   				= self.time_embedding(__UpperCamelCase						)

									if self.class_embedding is not None:
													if class_labels is None:
																	raise ValueError('class_labels should be provided when doing class conditioning'						)

													if self.config.class_embed_type == "timestep":
																	snake_case__   :      Optional[Any]   				= self.time_proj(__UpperCamelCase						)

													snake_case__   :      int   				= self.class_embedding(__UpperCamelCase						).to(dtype=self.dtype						)
													snake_case__   :      Dict   				= emb + class_emb

									# 2. pre-process
									snake_case__   :      List[Any]   				= sample
									snake_case__   :      Any   				= self.conv_in(__UpperCamelCase						)

									# 3. down
									snake_case__   :      Union[str, Any]   				= (sample,)
									for downsample_block in self.down_blocks:
													if hasattr(__UpperCamelCase       ,			'skip_conv'						):
																	snake_case__				,    snake_case__				,    snake_case__   :      Any   				= downsample_block(
																	    hidden_states=__UpperCamelCase       ,			temb=__UpperCamelCase       ,			skip_sample=__UpperCamelCase						)
													else:
																	snake_case__				,    snake_case__   :      Optional[int]   				= downsample_block(hidden_states=__UpperCamelCase       ,			temb=__UpperCamelCase						)

													down_block_res_samples += res_samples

									# 4. mid
									snake_case__   :      Union[str, Any]   				= self.mid_block(__UpperCamelCase       ,			__UpperCamelCase						)

									# 5. up
									snake_case__   :      str   				= None
									for upsample_block in self.up_blocks:
													snake_case__   :      Tuple   				= down_block_res_samples[-len(upsample_block.resnets						) :]
													snake_case__   :      str   				= down_block_res_samples[: -len(upsample_block.resnets						)]

													if hasattr(__UpperCamelCase       ,			'skip_conv'						):
																	snake_case__				,    snake_case__   :      Dict   				= upsample_block(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
													else:
																	snake_case__   :      List[Any]   				= upsample_block(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

        # 6. post-process
									snake_case__   :      List[Any]   				= self.conv_norm_out(__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= self.conv_act(__UpperCamelCase						)
									snake_case__   :      int   				= self.conv_out(__UpperCamelCase						)

									if skip_sample is not None:
													sample += skip_sample

									if self.config.time_embedding_type == "fourier":
													snake_case__   :      List[str]   				= timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]						)))						)
													snake_case__   :      Dict   				= sample / timesteps

									if not return_dict:
													return (sample,)

									return UNetaDOutput(sample=__UpperCamelCase						)



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						)


