






"""simple docstring"""

from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin


@dataclass
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       torch.FloatTensor







class 	__lowerCamelCase       (				A__				,							A__    ):





		'''simple docstring'''






		@register_to_config
		def __init__(     self							:			Union[str, Any]		,    a_							:			int = 16		,    a_							:			int = 88		,    a_							:			Optional[int] = None		,    a_							:			Optional[int] = None		,    a_							:			int = 1		,    a_							:			float = 0.0		,    a_							:			int = 32		,    a_							:			Optional[int] = None		,    a_							:			bool = False		,    a_							:			Optional[int] = None		,    a_							:			Optional[int] = None		,    a_							:			Optional[int] = None		,    a_							:			str = "geglu"		,    a_							:			Optional[int] = None		,    a_							:			bool = False		,    a_							:			bool = False		,    a_							:			bool = False		,    a_							:			str = "layer_norm"		,    a_							:			bool = True		,    ):
				super().__init__()
				lowerCAmelCase_     :							List[Any]             =    use_linear_projection
				lowerCAmelCase_     :							Optional[int]             =    num_attention_heads
				lowerCAmelCase_     :							str             =    attention_head_dim
				lowerCAmelCase_     :							List[Any]             =    num_attention_heads * attention_head_dim

				# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
				# Define whether input is continuous or discrete depending on configuration
				lowerCAmelCase_     :							int             =    (in_channels is not None) and (patch_size is None)
				lowerCAmelCase_     :							Dict             =    num_vector_embeds is not None
				lowerCAmelCase_     :							Optional[Any]             =    in_channels is not None and patch_size is not None

				if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
						lowerCAmelCase_     :							Dict             =    (
						    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
						    " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
						    " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
						    " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
						    " would be very nice if you could open a Pull request for the `transformer/config.json` file"
						)
						deprecate("norm_type!=num_embeds_ada_norm"		,    "1.0.0"		,    a_		,    standard_warn=a_			)
						lowerCAmelCase_     :							str             =    "ada_norm"

				if self.is_input_continuous and self.is_input_vectorized:
						raise ValueError(
						    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
						    " sure that either `in_channels` or `num_vector_embeds` is None."			)
				elif self.is_input_vectorized and self.is_input_patches:
						raise ValueError(
						    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
						    " sure that either `num_vector_embeds` or `num_patches` is None."			)
				elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
						raise ValueError(
						    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
						    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''			)

				# 2. Define input layers
				if self.is_input_continuous:
						lowerCAmelCase_     :							Optional[int]             =    in_channels

						lowerCAmelCase_     :							List[str]             =    torch.nn.GroupNorm(num_groups=a_		,    num_channels=a_		,    eps=1e-6		,    affine=a_			)
						if use_linear_projection:
								lowerCAmelCase_     :							Union[str, Any]             =    nn.Linear(a_		,    a_			)
						else:
								lowerCAmelCase_     :							Union[str, Any]             =    nn.Convad(a_		,    a_		,    kernel_size=1		,    stride=1		,    padding=0			)
				elif self.is_input_vectorized:
						assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
						assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

						lowerCAmelCase_     :							Optional[Any]             =    sample_size
						lowerCAmelCase_     :							int             =    sample_size
						lowerCAmelCase_     :							Union[str, Any]             =    num_vector_embeds
						lowerCAmelCase_     :							Union[str, Any]             =    self.height * self.width

						lowerCAmelCase_     :							int             =    ImagePositionalEmbeddings(
						    num_embed=a_		,    embed_dim=a_		,    height=self.height		,    width=self.width			)
				elif self.is_input_patches:
						assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

						lowerCAmelCase_     :							List[Any]             =    sample_size
						lowerCAmelCase_     :							List[Any]             =    sample_size

						lowerCAmelCase_     :							int             =    patch_size
						lowerCAmelCase_     :							str             =    PatchEmbed(
						    height=a_		,    width=a_		,    patch_size=a_		,    in_channels=a_		,    embed_dim=a_		,    )

				# 3. Define transformers blocks
				lowerCAmelCase_     :							Dict             =    nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            a_		,    a_		,    a_		,    dropout=a_		,    cross_attention_dim=a_		,    activation_fn=a_		,    num_embeds_ada_norm=a_		,    attention_bias=a_		,    only_cross_attention=a_		,    upcast_attention=a_		,    norm_type=a_		,    norm_elementwise_affine=a_		,    )
				        for d in range(a_			)
				    ]			)

				# 4. Define output layers
				lowerCAmelCase_     :							Optional[int]             =    in_channels if out_channels is None else out_channels
				if self.is_input_continuous:
						# TODO: should use out_channels for continuous projections
						if use_linear_projection:
								lowerCAmelCase_     :							Tuple             =    nn.Linear(a_		,    a_			)
						else:
								lowerCAmelCase_     :							int             =    nn.Convad(a_		,    a_		,    kernel_size=1		,    stride=1		,    padding=0			)
				elif self.is_input_vectorized:
						lowerCAmelCase_     :							Optional[int]             =    nn.LayerNorm(a_			)
						lowerCAmelCase_     :							Optional[int]             =    nn.Linear(a_		,    self.num_vector_embeds - 1			)
				elif self.is_input_patches:
						lowerCAmelCase_     :							List[Any]             =    nn.LayerNorm(a_		,    elementwise_affine=a_		,    eps=1e-6			)
						lowerCAmelCase_     :							List[str]             =    nn.Linear(a_		,    2 * inner_dim			)
						lowerCAmelCase_     :							Dict             =    nn.Linear(a_		,    patch_size * patch_size * self.out_channels			)





		def      lowerCamelCase (     self							:			Union[str, Any]		,    a_							:			torch.Tensor		,    a_							:			Optional[torch.Tensor] = None		,    a_							:			Optional[torch.LongTensor] = None		,    a_							:			Optional[torch.LongTensor] = None		,    a_							:			Dict[str, Any] = None		,    a_							:			Optional[torch.Tensor] = None		,    a_							:			Optional[torch.Tensor] = None		,    a_							:			bool = True		,    ):
				# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
				#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
				#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
				# expects mask of shape:
				#   [batch, key_tokens]
				# adds singleton query_tokens dimension:
				#   [batch,                    1, key_tokens]
				# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
				#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
				#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
				if attention_mask is not None and attention_mask.ndim == 2:
						# assume that mask is expressed as:
						#   (1 = keep,      0 = discard)
						# convert mask into a bias that can be added to attention scores:
						#       (keep = +0,     discard = -10000.0)
						lowerCAmelCase_     :							str             =    (1 - attention_mask.to(hidden_states.dtype			)) * -10000.0
						lowerCAmelCase_     :							Any             =    attention_mask.unsqueeze(1			)

				# convert encoder_attention_mask to a bias the same way we do for attention_mask
				if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
						lowerCAmelCase_     :							List[str]             =    (1 - encoder_attention_mask.to(hidden_states.dtype			)) * -10000.0
						lowerCAmelCase_     :							Union[str, Any]             =    encoder_attention_mask.unsqueeze(1			)

				# 1. Input
				if self.is_input_continuous:
						lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_     :							str             =    hidden_states.shape
						lowerCAmelCase_     :							Union[str, Any]             =    hidden_states

						lowerCAmelCase_     :							int             =    self.norm(a_			)
						if not self.use_linear_projection:
								lowerCAmelCase_     :							Optional[Any]             =    self.proj_in(a_			)
								lowerCAmelCase_     :							List[Any]             =    hidden_states.shape[1]
								lowerCAmelCase_     :							Optional[Any]             =    hidden_states.permute(0		,    2		,    3		,    1			).reshape(a_		,    height * width		,    a_			)
						else:
								lowerCAmelCase_     :							int             =    hidden_states.shape[1]
								lowerCAmelCase_     :							Dict             =    hidden_states.permute(0		,    2		,    3		,    1			).reshape(a_		,    height * width		,    a_			)
								lowerCAmelCase_     :							Tuple             =    self.proj_in(a_			)
				elif self.is_input_vectorized:
						lowerCAmelCase_     :							List[str]             =    self.latent_image_embedding(a_			)
				elif self.is_input_patches:
						lowerCAmelCase_     :							int             =    self.pos_embed(a_			)

				# 2. Blocks
				for block in self.transformer_blocks:
						lowerCAmelCase_     :							Optional[Any]             =    block(
						    a_		,    attention_mask=a_		,    encoder_hidden_states=a_		,    encoder_attention_mask=a_		,    timestep=a_		,    cross_attention_kwargs=a_		,    class_labels=a_		,    )

				# 3. Output
				if self.is_input_continuous:
						if not self.use_linear_projection:
								lowerCAmelCase_     :							str             =    hidden_states.reshape(a_		,    a_		,    a_		,    a_			).permute(0		,    3		,    1		,    2			).contiguous()
								lowerCAmelCase_     :							Optional[Any]             =    self.proj_out(a_			)
						else:
								lowerCAmelCase_     :							List[Any]             =    self.proj_out(a_			)
								lowerCAmelCase_     :							str             =    hidden_states.reshape(a_		,    a_		,    a_		,    a_			).permute(0		,    3		,    1		,    2			).contiguous()

						lowerCAmelCase_     :							List[str]             =    hidden_states + residual
				elif self.is_input_vectorized:
						lowerCAmelCase_     :							Tuple             =    self.norm_out(a_			)
						lowerCAmelCase_     :							List[Any]             =    self.out(a_			)
						# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
						lowerCAmelCase_     :							Optional[int]             =    logits.permute(0		,    2		,    1			)

						# log(p(x_0))
						lowerCAmelCase_     :							List[Any]             =    F.log_softmax(logits.double()		,    dim=1			).float()
				elif self.is_input_patches:
						# TODO: cleanup!
						lowerCAmelCase_     :							str             =    self.transformer_blocks[0].norma.emb(
						    a_		,    a_		,    hidden_dtype=hidden_states.dtype			)
						lowerCAmelCase_       ,			lowerCAmelCase_     :							int             =    self.proj_out_a(F.silu(a_			)			).chunk(2		,    dim=1			)
						lowerCAmelCase_     :							List[Any]             =    self.norm_out(a_			) * (1 + scale[:, None]) + shift[:, None]
						lowerCAmelCase_     :							Union[str, Any]             =    self.proj_out_a(a_			)

						# unpatchify
						lowerCAmelCase_     :							Tuple             =    int(hidden_states.shape[1] ** 0.5			)
						lowerCAmelCase_     :							Dict             =    hidden_states.reshape(
						    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)			)
						lowerCAmelCase_     :							str             =    torch.einsum("nhwpqc->nchpwq"		,    a_			)
						lowerCAmelCase_     :							Optional[int]             =    hidden_states.reshape(
						    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)			)

				if not return_dict:
						return (output,)

				return TransformeraDModelOutput(sample=a_			)








"""simple docstring"""

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 	__lowerCamelCase       (				A__				,							unittest.TestCase    ):





		'''simple docstring'''






		a_			:       List[str]      =							RealmTokenizer
		a_			:       int      =							RealmTokenizerFast
		a_			:       str      =							True
		a_			:       Tuple      =							True
		a_			:       List[Any]      =							filter_non_english





		def      lowerCamelCase (     self							:			int			):
				super().setUp()

				lowerCAmelCase_     :							int             =    [
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				lowerCAmelCase_     :							List[Any]             =    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      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Any			):
				lowerCAmelCase_     :							int             =    "UNwant\u00E9d,running"
				lowerCAmelCase_     :							Optional[Any]             =    "unwanted, running"
				return input_text, output_text





		def      lowerCamelCase (     self							:			int			):
				lowerCAmelCase_     :							Tuple             =    self.tokenizer_class(self.vocab_file			)

				lowerCAmelCase_     :							List[str]             =    tokenizer.tokenize("UNwant\u00E9d,running"			)
				self.assertListEqual(a_		,    ["un", "##want", "##ed", ",", "runn", "##ing"]			)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)		,    [9, 6, 7, 12, 10, 11]			)





		def      lowerCamelCase (     self							:			Dict			):
				if not self.test_rust_tokenizer:
						return

				lowerCAmelCase_     :							str             =    self.get_tokenizer()
				lowerCAmelCase_     :							List[str]             =    self.get_rust_tokenizer()

				lowerCAmelCase_     :							Optional[Any]             =    "UNwant\u00E9d,running"

				lowerCAmelCase_     :							Optional[Any]             =    tokenizer.tokenize(a_			)
				lowerCAmelCase_     :							List[Any]             =    rust_tokenizer.tokenize(a_			)
				self.assertListEqual(a_		,    a_			)

				lowerCAmelCase_     :							str             =    tokenizer.encode(a_		,    add_special_tokens=a_			)
				lowerCAmelCase_     :							Dict             =    rust_tokenizer.encode(a_		,    add_special_tokens=a_			)
				self.assertListEqual(a_		,    a_			)

				lowerCAmelCase_     :							int             =    self.get_rust_tokenizer()
				lowerCAmelCase_     :							Any             =    tokenizer.encode(a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    rust_tokenizer.encode(a_			)
				self.assertListEqual(a_		,    a_			)

				# With lower casing
				lowerCAmelCase_     :							Dict             =    self.get_tokenizer(do_lower_case=a_			)
				lowerCAmelCase_     :							List[str]             =    self.get_rust_tokenizer(do_lower_case=a_			)

				lowerCAmelCase_     :							Dict             =    "UNwant\u00E9d,running"

				lowerCAmelCase_     :							Union[str, Any]             =    tokenizer.tokenize(a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    rust_tokenizer.tokenize(a_			)
				self.assertListEqual(a_		,    a_			)

				lowerCAmelCase_     :							Optional[Any]             =    tokenizer.encode(a_		,    add_special_tokens=a_			)
				lowerCAmelCase_     :							Dict             =    rust_tokenizer.encode(a_		,    add_special_tokens=a_			)
				self.assertListEqual(a_		,    a_			)

				lowerCAmelCase_     :							str             =    self.get_rust_tokenizer()
				lowerCAmelCase_     :							str             =    tokenizer.encode(a_			)
				lowerCAmelCase_     :							Optional[int]             =    rust_tokenizer.encode(a_			)
				self.assertListEqual(a_		,    a_			)





		def      lowerCamelCase (     self							:			Optional[int]			):
				lowerCAmelCase_     :							Any             =    BasicTokenizer()

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





		def      lowerCamelCase (     self							:			Tuple			):
				lowerCAmelCase_     :							Dict             =    BasicTokenizer(do_lower_case=a_			)

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





		def      lowerCamelCase (     self							:			Optional[Any]			):
				lowerCAmelCase_     :							Optional[int]             =    BasicTokenizer(do_lower_case=a_		,    strip_accents=a_			)

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





		def      lowerCamelCase (     self							:			Tuple			):
				lowerCAmelCase_     :							Dict             =    BasicTokenizer(do_lower_case=a_		,    strip_accents=a_			)

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





		def      lowerCamelCase (     self							:			str			):
				lowerCAmelCase_     :							List[Any]             =    BasicTokenizer(do_lower_case=a_			)

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





		def      lowerCamelCase (     self							:			List[Any]			):
				lowerCAmelCase_     :							Optional[Any]             =    BasicTokenizer(do_lower_case=a_			)

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





		def      lowerCamelCase (     self							:			List[Any]			):
				lowerCAmelCase_     :							List[Any]             =    BasicTokenizer(do_lower_case=a_		,    strip_accents=a_			)

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





		def      lowerCamelCase (     self							:			Dict			):
				lowerCAmelCase_     :							List[str]             =    BasicTokenizer(do_lower_case=a_		,    strip_accents=a_			)

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





		def      lowerCamelCase (     self							:			List[str]			):
				lowerCAmelCase_     :							Any             =    BasicTokenizer(do_lower_case=a_		,    never_split=["[UNK]"]			)

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





		def      lowerCamelCase (     self							:			Optional[Any]			):
				lowerCAmelCase_     :							int             =    ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

				lowerCAmelCase_     :							Union[str, Any]             =    {}
				for i, token in enumerate(a_			):
						lowerCAmelCase_     :							Any             =    i
				lowerCAmelCase_     :							int             =    WordpieceTokenizer(vocab=a_		,    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      lowerCamelCase (     self							:			Tuple			):
				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      lowerCamelCase (     self							:			Optional[Any]			):
				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      lowerCamelCase (     self							:			str			):
				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      lowerCamelCase (     self							:			str			):
				lowerCAmelCase_     :							Any             =    self.get_tokenizer()

				# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
				self.assertListEqual([tokenizer.tokenize(a_			) for t in ["Test", "\xad", "test"]]		,    [["[UNK]"], [], ["[UNK]"]]			)

				if self.test_rust_tokenizer:
						lowerCAmelCase_     :							str             =    self.get_rust_tokenizer()
						self.assertListEqual(
						    [rust_tokenizer.tokenize(a_			) for t in ["Test", "\xad", "test"]]		,    [["[UNK]"], [], ["[UNK]"]]			)





		@slow
		def      lowerCamelCase (     self							:			int			):
				lowerCAmelCase_     :							int             =    self.tokenizer_class.from_pretrained("bert-base-uncased"			)

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

				lowerCAmelCase_     :							Dict             =    tokenizer.build_inputs_with_special_tokens(a_			)
				lowerCAmelCase_     :							Tuple             =    tokenizer.build_inputs_with_special_tokens(a_		,    a_			)

				assert encoded_sentence == [1_01] + text + [1_02]
				assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]





		def      lowerCamelCase (     self							:			Optional[int]			):
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
								lowerCAmelCase_     :							Tuple             =    self.rust_tokenizer_class.from_pretrained(a_		,    **a_			)

								lowerCAmelCase_     :							Tuple             =    f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
								lowerCAmelCase_     :							List[str]             =    tokenizer_r.encode_plus(
								    a_		,    return_attention_mask=a_		,    return_token_type_ids=a_		,    return_offsets_mapping=a_		,    add_special_tokens=a_		,    )

								lowerCAmelCase_     :							List[str]             =    tokenizer_r.do_lower_case if hasattr(a_		,    "do_lower_case"			) else False
								lowerCAmelCase_     :							int             =    (
								    [
								        ((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      lowerCamelCase (     self							:			int			):
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
								lowerCAmelCase_     :							Tuple             =    self.rust_tokenizer_class.from_pretrained(a_		,    **a_			)
								lowerCAmelCase_     :							Tuple             =    self.tokenizer_class.from_pretrained(a_		,    **a_			)
								lowerCAmelCase_     :							Dict             =    [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]

								lowerCAmelCase_     :							Optional[Any]             =    tokenizer_r.batch_encode_candidates(a_		,    max_length=10		,    return_tensors="np"			)
								lowerCAmelCase_     :							int             =    tokenizer_p.batch_encode_candidates(a_		,    max_length=10		,    return_tensors="np"			)

								lowerCAmelCase_     :							Dict             =    (2, 2, 10)

								self.assertEqual(encoded_sentence_r["input_ids"].shape		,    a_			)
								self.assertEqual(encoded_sentence_r["attention_mask"].shape		,    a_			)
								self.assertEqual(encoded_sentence_r["token_type_ids"].shape		,    a_			)

								self.assertEqual(encoded_sentence_p["input_ids"].shape		,    a_			)
								self.assertEqual(encoded_sentence_p["attention_mask"].shape		,    a_			)
								self.assertEqual(encoded_sentence_p["token_type_ids"].shape		,    a_			)
