






from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block





@dataclass
class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      42





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase=3       ,    UpperCAmelCase=3       ,    UpperCAmelCase=("DownEncoderBlock2D",)       ,    UpperCAmelCase=(64,)       ,    UpperCAmelCase=2       ,    UpperCAmelCase=32       ,    UpperCAmelCase="silu"       ,    UpperCAmelCase=True       ,    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			layers_per_block

							_UpperCAmelCase									=			torch.nn.Convad(
							    UpperCAmelCase       ,    block_out_channels[0]       ,    kernel_size=3       ,    stride=1       ,    padding=1       ,    )

							_UpperCAmelCase									=			None
							_UpperCAmelCase									=			nn.ModuleList([]    )

							# down
							_UpperCAmelCase									=			block_out_channels[0]
							for i, down_block_type in enumerate(UpperCAmelCase    ):
													_UpperCAmelCase									=			output_channel
													_UpperCAmelCase									=			block_out_channels[i]
													_UpperCAmelCase									=			i == len(UpperCAmelCase    ) - 1

													_UpperCAmelCase									=			get_down_block(
													    UpperCAmelCase       ,    num_layers=self.layers_per_block       ,    in_channels=UpperCAmelCase       ,    out_channels=UpperCAmelCase       ,    add_downsample=not is_final_block       ,    resnet_eps=1e-6       ,    downsample_padding=0       ,    resnet_act_fn=UpperCAmelCase       ,    resnet_groups=UpperCAmelCase       ,    attention_head_dim=UpperCAmelCase       ,    temb_channels=UpperCAmelCase       ,    )
													self.down_blocks.append(UpperCAmelCase    )

							# mid
							_UpperCAmelCase									=			UNetMidBlockaD(
							    in_channels=block_out_channels[-1]       ,    resnet_eps=1e-6       ,    resnet_act_fn=UpperCAmelCase       ,    output_scale_factor=1       ,    resnet_time_scale_shift='default'       ,    attention_head_dim=block_out_channels[-1]       ,    resnet_groups=UpperCAmelCase       ,    temb_channels=UpperCAmelCase       ,    )

							# out
							_UpperCAmelCase									=			nn.GroupNorm(num_channels=block_out_channels[-1]       ,    num_groups=UpperCAmelCase       ,    eps=1e-6    )
							_UpperCAmelCase									=			nn.SiLU()

							_UpperCAmelCase									=			2 * out_channels if double_z else out_channels
							_UpperCAmelCase									=			nn.Convad(block_out_channels[-1]       ,    UpperCAmelCase       ,    3       ,    padding=1    )

							_UpperCAmelCase									=			False

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			x
							_UpperCAmelCase									=			self.conv_in(UpperCAmelCase    )

							if self.training and self.gradient_checkpointing:

													def create_custom_forward(UpperCAmelCase    ):
																			def custom_forward(*UpperCAmelCase    ):
																									return module(*UpperCAmelCase    )

																			return custom_forward

													# down
													if is_torch_version('>='       ,    '1.11.0'    ):
																			for down_block in self.down_blocks:
																									_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(
																									    create_custom_forward(UpperCAmelCase    )       ,    UpperCAmelCase       ,    use_reentrant=UpperCAmelCase    )
																			# middle
																			_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block    )       ,    UpperCAmelCase       ,    use_reentrant=UpperCAmelCase    )
													else:
																			for down_block in self.down_blocks:
																									_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase    )       ,    UpperCAmelCase    )
																			# middle
																			_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block    )       ,    UpperCAmelCase    )

							else:
													# down
													for down_block in self.down_blocks:
																			_UpperCAmelCase									=			down_block(UpperCAmelCase    )

													# middle
													_UpperCAmelCase									=			self.mid_block(UpperCAmelCase    )

							# post-process
							_UpperCAmelCase									=			self.conv_norm_out(UpperCAmelCase    )
							_UpperCAmelCase									=			self.conv_act(UpperCAmelCase    )
							_UpperCAmelCase									=			self.conv_out(UpperCAmelCase    )

							return sample





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase=3       ,    UpperCAmelCase=3       ,    UpperCAmelCase=("UpDecoderBlock2D",)       ,    UpperCAmelCase=(64,)       ,    UpperCAmelCase=2       ,    UpperCAmelCase=32       ,    UpperCAmelCase="silu"       ,    UpperCAmelCase="group"       ,    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			layers_per_block

							_UpperCAmelCase									=			nn.Convad(
							    UpperCAmelCase       ,    block_out_channels[-1]       ,    kernel_size=3       ,    stride=1       ,    padding=1       ,    )

							_UpperCAmelCase									=			None
							_UpperCAmelCase									=			nn.ModuleList([]    )

							_UpperCAmelCase									=			in_channels if norm_type == 'spatial' else None

							# mid
							_UpperCAmelCase									=			UNetMidBlockaD(
							    in_channels=block_out_channels[-1]       ,    resnet_eps=1e-6       ,    resnet_act_fn=UpperCAmelCase       ,    output_scale_factor=1       ,    resnet_time_scale_shift='default' if norm_type == 'group' else norm_type       ,    attention_head_dim=block_out_channels[-1]       ,    resnet_groups=UpperCAmelCase       ,    temb_channels=UpperCAmelCase       ,    )

							# up
							_UpperCAmelCase									=			list(reversed(UpperCAmelCase    )    )
							_UpperCAmelCase									=			reversed_block_out_channels[0]
							for i, up_block_type in enumerate(UpperCAmelCase    ):
													_UpperCAmelCase									=			output_channel
													_UpperCAmelCase									=			reversed_block_out_channels[i]

													_UpperCAmelCase									=			i == len(UpperCAmelCase    ) - 1

													_UpperCAmelCase									=			get_up_block(
													    UpperCAmelCase       ,    num_layers=self.layers_per_block + 1       ,    in_channels=UpperCAmelCase       ,    out_channels=UpperCAmelCase       ,    prev_output_channel=UpperCAmelCase       ,    add_upsample=not is_final_block       ,    resnet_eps=1e-6       ,    resnet_act_fn=UpperCAmelCase       ,    resnet_groups=UpperCAmelCase       ,    attention_head_dim=UpperCAmelCase       ,    temb_channels=UpperCAmelCase       ,    resnet_time_scale_shift=UpperCAmelCase       ,    )
													self.up_blocks.append(UpperCAmelCase    )
													_UpperCAmelCase									=			output_channel

							# out
							if norm_type == "spatial":
													_UpperCAmelCase									=			SpatialNorm(block_out_channels[0]       ,    UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			nn.GroupNorm(num_channels=block_out_channels[0]       ,    num_groups=UpperCAmelCase       ,    eps=1e-6    )
							_UpperCAmelCase									=			nn.SiLU()
							_UpperCAmelCase									=			nn.Convad(block_out_channels[0]       ,    UpperCAmelCase       ,    3       ,    padding=1    )

							_UpperCAmelCase									=			False

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			z
							_UpperCAmelCase									=			self.conv_in(UpperCAmelCase    )

							_UpperCAmelCase									=			next(iter(self.up_blocks.parameters()    )    ).dtype
							if self.training and self.gradient_checkpointing:

													def create_custom_forward(UpperCAmelCase    ):
																			def custom_forward(*UpperCAmelCase    ):
																									return module(*UpperCAmelCase    )

																			return custom_forward

													if is_torch_version('>='       ,    '1.11.0'    ):
																			# middle
																			_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block    )       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    use_reentrant=UpperCAmelCase    )
																			_UpperCAmelCase									=			sample.to(UpperCAmelCase    )

																			# up
																			for up_block in self.up_blocks:
																									_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(
																									    create_custom_forward(UpperCAmelCase    )       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    use_reentrant=UpperCAmelCase    )
													else:
																			# middle
																			_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block    )       ,    UpperCAmelCase       ,    UpperCAmelCase    )
																			_UpperCAmelCase									=			sample.to(UpperCAmelCase    )

																			# up
																			for up_block in self.up_blocks:
																									_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase    )       ,    UpperCAmelCase       ,    UpperCAmelCase    )
							else:
													# middle
													_UpperCAmelCase									=			self.mid_block(UpperCAmelCase       ,    UpperCAmelCase    )
													_UpperCAmelCase									=			sample.to(UpperCAmelCase    )

													# up
													for up_block in self.up_blocks:
																			_UpperCAmelCase									=			up_block(UpperCAmelCase       ,    UpperCAmelCase    )

        # post-process
							if latent_embeds is None:
													_UpperCAmelCase									=			self.conv_norm_out(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			self.conv_norm_out(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			self.conv_act(UpperCAmelCase    )
							_UpperCAmelCase									=			self.conv_out(UpperCAmelCase    )

							return sample





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase=None       ,    UpperCAmelCase="random"       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			n_e
							_UpperCAmelCase									=			vq_embed_dim
							_UpperCAmelCase									=			beta
							_UpperCAmelCase									=			legacy

							_UpperCAmelCase									=			nn.Embedding(self.n_e       ,    self.vq_embed_dim    )
							self.embedding.weight.data.uniform_(-1.0 / self.n_e       ,    1.0 / self.n_e    )

							_UpperCAmelCase									=			remap
							if self.remap is not None:
													self.register_buffer('used'       ,    torch.tensor(np.load(self.remap    )    )    )
													_UpperCAmelCase									=			self.used.shape[0]
													_UpperCAmelCase									=			unknown_index  # "random" or "extra" or integer
													if self.unknown_index == "extra":
																			_UpperCAmelCase									=			self.re_embed
																			_UpperCAmelCase									=			self.re_embed + 1
													print(
													    F"""Remapping {self.n_e} indices to {self.re_embed} indices. """
													    F"""Using {self.unknown_index} for unknown indices."""    )
							else:
													_UpperCAmelCase									=			n_e

							_UpperCAmelCase									=			sane_index_shape

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			inds.shape
							assert len(UpperCAmelCase    ) > 1
							_UpperCAmelCase									=			inds.reshape(ishape[0]       ,    -1    )
							_UpperCAmelCase									=			self.used.to(UpperCAmelCase    )
							_UpperCAmelCase									=			(inds[:, :, None] == used[None, None, ...]).long()
							_UpperCAmelCase									=			match.argmax(-1    )
							_UpperCAmelCase									=			match.sum(2    ) < 1
							if self.unknown_index == "random":
													_UpperCAmelCase									=			torch.randint(0       ,    self.re_embed       ,    size=new[unknown].shape    ).to(device=new.device    )
							else:
													_UpperCAmelCase									=			self.unknown_index
							return new.reshape(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			inds.shape
							assert len(UpperCAmelCase    ) > 1
							_UpperCAmelCase									=			inds.reshape(ishape[0]       ,    -1    )
							_UpperCAmelCase									=			self.used.to(UpperCAmelCase    )
							if self.re_embed > self.used.shape[0]:  # extra token
													_UpperCAmelCase									=			0  # simply set to zero
							_UpperCAmelCase									=			torch.gather(used[None, :][inds.shape[0] * [0], :]       ,    1       ,    UpperCAmelCase    )
							return back.reshape(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			z.permute(0       ,    2       ,    3       ,    1    ).contiguous()
							_UpperCAmelCase									=			z.view(-1       ,    self.vq_embed_dim    )

							# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
							_UpperCAmelCase									=			torch.argmin(torch.cdist(UpperCAmelCase       ,    self.embedding.weight    )       ,    dim=1    )

							_UpperCAmelCase									=			self.embedding(UpperCAmelCase    ).view(z.shape    )
							_UpperCAmelCase									=			None
							_UpperCAmelCase									=			None

							# compute loss for embedding
							if not self.legacy:
													_UpperCAmelCase									=			self.beta * torch.mean((z_q.detach() - z) ** 2    ) + torch.mean((z_q - z.detach()) ** 2    )
							else:
													_UpperCAmelCase									=			torch.mean((z_q.detach() - z) ** 2    ) + self.beta * torch.mean((z_q - z.detach()) ** 2    )

							# preserve gradients
							_UpperCAmelCase									=			z + (z_q - z).detach()

							# reshape back to match original input shape
							_UpperCAmelCase									=			z_q.permute(0       ,    3       ,    1       ,    2    ).contiguous()

							if self.remap is not None:
													_UpperCAmelCase									=			min_encoding_indices.reshape(z.shape[0]       ,    -1    )  # add batch axis
													_UpperCAmelCase									=			self.remap_to_used(UpperCAmelCase    )
													_UpperCAmelCase									=			min_encoding_indices.reshape(-1       ,    1    )  # flatten

							if self.sane_index_shape:
													_UpperCAmelCase									=			min_encoding_indices.reshape(z_q.shape[0]       ,    z_q.shape[2]       ,    z_q.shape[3]    )

							return z_q, loss, (perplexity, min_encodings, min_encoding_indices)

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if self.remap is not None:
													_UpperCAmelCase									=			indices.reshape(shape[0]       ,    -1    )  # add batch axis
													_UpperCAmelCase									=			self.unmap_to_all(UpperCAmelCase    )
													_UpperCAmelCase									=			indices.reshape(-1    )  # flatten again

							# get quantized latent vectors
							_UpperCAmelCase									=			self.embedding(UpperCAmelCase    )

							if shape is not None:
													_UpperCAmelCase									=			z_q.view(UpperCAmelCase    )
													# reshape back to match original input shape
													_UpperCAmelCase									=			z_q.permute(0       ,    3       ,    1       ,    2    ).contiguous()

							return z_q





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=False    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parameters
							_UpperCAmelCase					, _UpperCAmelCase									=			torch.chunk(UpperCAmelCase       ,    2       ,    dim=1    )
							_UpperCAmelCase									=			torch.clamp(self.logvar       ,    -30.0       ,    20.0    )
							_UpperCAmelCase									=			deterministic
							_UpperCAmelCase									=			torch.exp(0.5 * self.logvar    )
							_UpperCAmelCase									=			torch.exp(self.logvar    )
							if self.deterministic:
													_UpperCAmelCase									=			_UpperCAmelCase									=			torch.zeros_like(
													    self.mean       ,    device=self.parameters.device       ,    dtype=self.parameters.dtype    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			randn_tensor(
							    self.mean.shape       ,    generator=UpperCAmelCase       ,    device=self.parameters.device       ,    dtype=self.parameters.dtype    )
							_UpperCAmelCase									=			self.mean + self.std * sample
							return x

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							if self.deterministic:
													return torch.Tensor([0.0]    )
							else:
													if other is None:
																			return 0.5 * torch.sum(torch.pow(self.mean       ,    2    ) + self.var - 1.0 - self.logvar       ,    dim=[1, 2, 3]    )
													else:
																			return 0.5 * torch.sum(
																			    torch.pow(self.mean - other.mean       ,    2    ) / other.var
																			    + self.var / other.var
																			    - 1.0
																			    - self.logvar
																			    + other.logvar       ,    dim=[1, 2, 3]       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=[1, 2, 3]    ):
							"""simple docstring"""







							if self.deterministic:
													return torch.Tensor([0.0]    )
							_UpperCAmelCase									=			np.log(2.0 * np.pi    )
							return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean       ,    2    ) / self.var       ,    dim=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self.mean








import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a      	=  get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      AlbertTokenizer
	UpperCamelCase__								=      AlbertTokenizerFast
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase    )
							tokenizer.save_pretrained(self.tmpdirname    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'this is a test'
							_UpperCAmelCase									=			'this is a test'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<pad>'
							_UpperCAmelCase									=			0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase    )       ,    UpperCAmelCase    )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			list(self.get_tokenizer().get_vocab().keys()    )

							self.assertEqual(vocab_keys[0]       ,    '<pad>'    )
							self.assertEqual(vocab_keys[1]       ,    '<unk>'    )
							self.assertEqual(vocab_keys[-1]       ,    '▁eloquent'    )
							self.assertEqual(len(UpperCAmelCase    )       ,    3_0000    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.assertEqual(self.get_tokenizer().vocab_size       ,    3_0000    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							if not self.test_rust_tokenizer:
													return

							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			self.get_rust_tokenizer()

							_UpperCAmelCase									=			'I was born in 92000, and this is falsé.'

							_UpperCAmelCase									=			tokenizer.tokenize(UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.tokenize(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.encode(UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.encode(UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_rust_tokenizer()
							_UpperCAmelCase									=			tokenizer.encode(UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.encode(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase       ,    keep_accents=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('This is a test'    )
							self.assertListEqual(UpperCAmelCase       ,    ['▁this', '▁is', '▁a', '▁test']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [48, 25, 21, 1289]    )

							_UpperCAmelCase									=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
							self.assertListEqual(
							    UpperCAmelCase       ,    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']    )
							_UpperCAmelCase									=			tokenizer.convert_tokens_to_ids(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

							_UpperCAmelCase									=			tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
							self.assertListEqual(
							    UpperCAmelCase       ,    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.encode('sequence builders'    )
							_UpperCAmelCase									=			tokenizer.encode('multi-sequence build'    )

							_UpperCAmelCase									=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       ,    UpperCAmelCase    )

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
							    tokenizer.sep_token_id
							]

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=UpperCAmelCase       ,    model_name='albert-base-v2'       ,    revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'       ,    )
