



'''simple docstring'''




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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class 		_UpperCamelCase						(							__a					):


			'''simple docstring'''

			_A   :	"DiagonalGaussianDistribution"






class 		_UpperCamelCase						(							__a					,      __a					):


			'''simple docstring'''

			_A   :	List[str]    					=	True

			@register_to_config
			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				int = 3							,  lowerCAmelCase__  :				Union[str, Any] = 3							,  lowerCAmelCase__  :				str = ("DownEncoderBlock2D",)							,  lowerCAmelCase__  :				Optional[int] = ("UpDecoderBlock2D",)							,  lowerCAmelCase__  :				Optional[Any] = (6_4,)							,  lowerCAmelCase__  :				Union[str, Any] = 1							,  lowerCAmelCase__  :				Any = "silu"							,  lowerCAmelCase__  :				Any = 4							,  lowerCAmelCase__  :				List[Any] = 3_2							,  lowerCAmelCase__  :				str = 3_2							,  lowerCAmelCase__  :				Any = 0.1_82_15							,  ):





					"""simple docstring"""






					super().__init__()

					# pass init params to Encoder
					__SCREAMING_SNAKE_CASE :	str					 =	Encoder(
					    in_channels=lowerCAmelCase_							,  out_channels=lowerCAmelCase_							,  down_block_types=lowerCAmelCase_							,  block_out_channels=lowerCAmelCase_							,  layers_per_block=lowerCAmelCase_							,  act_fn=lowerCAmelCase_							,  norm_num_groups=lowerCAmelCase_							,  double_z=lowerCAmelCase_							,  )

					# pass init params to Decoder
					__SCREAMING_SNAKE_CASE :	List[str]					 =	Decoder(
					    in_channels=lowerCAmelCase_							,  out_channels=lowerCAmelCase_							,  up_block_types=lowerCAmelCase_							,  block_out_channels=lowerCAmelCase_							,  layers_per_block=lowerCAmelCase_							,  norm_num_groups=lowerCAmelCase_							,  act_fn=lowerCAmelCase_							,  )

					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Convad(2 * latent_channels							,  2 * latent_channels							,  1		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.Convad(lowerCAmelCase_							,  lowerCAmelCase_							,  1		)

					__SCREAMING_SNAKE_CASE :	int					 =	False
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	False

					# only relevant if vae tiling is enabled
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.config.sample_size
					__SCREAMING_SNAKE_CASE :	List[str]					 =	(
					    self.config.sample_size[0]
					    if isinstance(self.config.sample_size							,  (list, tuple)		)
					    else self.config.sample_size
					)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	int(sample_size / (2 ** (len(self.config.block_out_channels		) - 1))		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	0.25

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Union[str, Any]=False		):





					"""simple docstring"""






					if isinstance(lowerCAmelCase_							,  (Encoder, Decoder)		):
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	value

			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				int = True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	use_tiling

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					self.enable_tiling(lowerCAmelCase_		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	True

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	False

			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{}

					def fn_recursive_add_processors(lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Optional[Any]		):
							if hasattr(lowerCAmelCase_							,  """set_processor"""		):
									__SCREAMING_SNAKE_CASE :	str					 =	module.processor

							for sub_name, child in module.named_children():
									fn_recursive_add_processors(F"{name}.{sub_name}"							,  lowerCAmelCase_							,  lowerCAmelCase_		)

							return processors

					for name, module in self.named_children():
							fn_recursive_add_processors(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_		)

					return processors

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	len(self.attn_processors.keys()		)

					if isinstance(lowerCAmelCase_							,  lowerCAmelCase_		) and len(lowerCAmelCase_		) != count:
							raise ValueError(
							    F"A dict of processors was passed, but the number of processors {len(lowerCAmelCase_		)} does not match the"
							    F" number of attention layers: {count}. Please make sure to pass {count} processor classes."		)

					def fn_recursive_attn_processor(lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				int		):
							if hasattr(lowerCAmelCase_							,  """set_processor"""		):
									if not isinstance(lowerCAmelCase_							,  lowerCAmelCase_		):
											module.set_processor(lowerCAmelCase_		)
									else:
											module.set_processor(processor.pop(F"{name}.processor"		)		)

							for sub_name, child in module.named_children():
									fn_recursive_attn_processor(F"{name}.{sub_name}"							,  lowerCAmelCase_							,  lowerCAmelCase_		)

					for name, module in self.named_children():
							fn_recursive_attn_processor(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					self.set_attn_processor(AttnProcessor()		)

			@apply_forward_hook
			def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				List[str] = True		):





					"""simple docstring"""






					if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
							return self.tiled_encode(lowerCAmelCase_							,  return_dict=lowerCAmelCase_		)

					if self.use_slicing and x.shape[0] > 1:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	[self.encoder(lowerCAmelCase_		) for x_slice in x.split(1		)]
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.cat(lowerCAmelCase_		)
					else:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.encoder(lowerCAmelCase_		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.quant_conv(lowerCAmelCase_		)
					__SCREAMING_SNAKE_CASE :	Any					 =	DiagonalGaussianDistribution(lowerCAmelCase_		)

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=lowerCAmelCase_		)

			def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Optional[Any] = True		):





					"""simple docstring"""






					if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
							return self.tiled_decode(lowerCAmelCase_							,  return_dict=lowerCAmelCase_		)

					__SCREAMING_SNAKE_CASE :	Any					 =	self.post_quant_conv(lowerCAmelCase_		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.decoder(lowerCAmelCase_		)

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=lowerCAmelCase_		)

			@apply_forward_hook
			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				List[Any] = True		):





					"""simple docstring"""






					if self.use_slicing and z.shape[0] > 1:
							__SCREAMING_SNAKE_CASE :	Dict					 =	[self._decode(lowerCAmelCase_		).sample for z_slice in z.split(1		)]
							__SCREAMING_SNAKE_CASE :	str					 =	torch.cat(lowerCAmelCase_		)
					else:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	self._decode(lowerCAmelCase_		).sample

					if not return_dict:
							return (decoded,)

					return DecoderOutput(sample=lowerCAmelCase_		)

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	min(a.shape[2]							,  b.shape[2]							,  lowerCAmelCase_		)
					for y in range(lowerCAmelCase_		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
					return b

			def UpperCamelCase__      (							self  :				Tuple							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	min(a.shape[3]							,  b.shape[3]							,  lowerCAmelCase_		)
					for x in range(lowerCAmelCase_		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
					return b

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				List[str] = True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	int(self.tile_latent_min_size * self.tile_overlap_factor		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.tile_latent_min_size - blend_extent

					# Split the image into 512x512 tiles and encode them separately.
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[]
					for i in range(0							,  x.shape[2]							,  lowerCAmelCase_		):
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[]
							for j in range(0							,  x.shape[3]							,  lowerCAmelCase_		):
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.encoder(lowerCAmelCase_		)
									__SCREAMING_SNAKE_CASE :	int					 =	self.quant_conv(lowerCAmelCase_		)
									row.append(lowerCAmelCase_		)
							rows.append(lowerCAmelCase_		)
					__SCREAMING_SNAKE_CASE :	Any					 =	[]
					for i, row in enumerate(lowerCAmelCase_		):
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[]
							for j, tile in enumerate(lowerCAmelCase_		):
									# blend the above tile and the left tile
									# to the current tile and add the current tile to the result row
									if i > 0:
											__SCREAMING_SNAKE_CASE :	int					 =	self.blend_v(rows[i - 1][j]							,  lowerCAmelCase_							,  lowerCAmelCase_		)
									if j > 0:
											__SCREAMING_SNAKE_CASE :	int					 =	self.blend_h(row[j - 1]							,  lowerCAmelCase_							,  lowerCAmelCase_		)
									result_row.append(tile[:, :, :row_limit, :row_limit]		)
							result_rows.append(torch.cat(lowerCAmelCase_							,  dim=3		)		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.cat(lowerCAmelCase_							,  dim=2		)
					__SCREAMING_SNAKE_CASE :	str					 =	DiagonalGaussianDistribution(lowerCAmelCase_		)

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=lowerCAmelCase_		)

			def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Optional[int] = True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	int(self.tile_sample_min_size * self.tile_overlap_factor		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.tile_sample_min_size - blend_extent

					# Split z into overlapping 64x64 tiles and decode them separately.
					# The tiles have an overlap to avoid seams between tiles.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	[]
					for i in range(0							,  z.shape[2]							,  lowerCAmelCase_		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	[]
							for j in range(0							,  z.shape[3]							,  lowerCAmelCase_		):
									__SCREAMING_SNAKE_CASE :	Tuple					 =	z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.post_quant_conv(lowerCAmelCase_		)
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.decoder(lowerCAmelCase_		)
									row.append(lowerCAmelCase_		)
							rows.append(lowerCAmelCase_		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[]
					for i, row in enumerate(lowerCAmelCase_		):
							__SCREAMING_SNAKE_CASE :	int					 =	[]
							for j, tile in enumerate(lowerCAmelCase_		):
									# blend the above tile and the left tile
									# to the current tile and add the current tile to the result row
									if i > 0:
											__SCREAMING_SNAKE_CASE :	List[str]					 =	self.blend_v(rows[i - 1][j]							,  lowerCAmelCase_							,  lowerCAmelCase_		)
									if j > 0:
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.blend_h(row[j - 1]							,  lowerCAmelCase_							,  lowerCAmelCase_		)
									result_row.append(tile[:, :, :row_limit, :row_limit]		)
							result_rows.append(torch.cat(lowerCAmelCase_							,  dim=3		)		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.cat(lowerCAmelCase_							,  dim=2		)
					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=lowerCAmelCase_		)



			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Optional[int] = False							,  lowerCAmelCase__  :				List[Any] = True							,  lowerCAmelCase__  :				Union[str, Any] = None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	sample
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.encode(lowerCAmelCase_		).latent_dist
					if sample_posterior:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	posterior.sample(generator=lowerCAmelCase_		)
					else:
							__SCREAMING_SNAKE_CASE :	Dict					 =	posterior.mode()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.decode(lowerCAmelCase_		).sample

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=lowerCAmelCase_		)









'''simple docstring'''




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__ :			Any												=				'''▁'''

UpperCamelCase__ :			Dict												=				get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Dict    					=	BertGenerationTokenizer
			_A   :	List[Any]    					=	False
			_A   :	Union[str, Any]    					=	True

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().setUp()

					__SCREAMING_SNAKE_CASE :	Dict					 =	BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__		)
					tokenizer.save_pretrained(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	"""<s>"""
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	1

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

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]							,  """<unk>"""		)
					self.assertEqual(vocab_keys[1]							,  """<s>"""		)
					self.assertEqual(vocab_keys[-1]							,  """<pad>"""		)
					self.assertEqual(len(lowerCAmelCase__		)							,  1_0_0_2		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_0_0		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.tokenize("""This is a test"""		)
					self.assertListEqual(lowerCAmelCase__							,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)							,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,  )

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """9""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """é""",
					        """.""",
					    ]							,  )
					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,  )

					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """<unk>""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """<unk>""",
					        """.""",
					    ]							,  )

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""		)

			@slow
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	"""Hello World!"""
					__SCREAMING_SNAKE_CASE :	Dict					 =	[1_8_5_3_6, 2_2_6_0, 1_0_1]

					self.assertListEqual(lowerCAmelCase__							,  self.big_tokenizer.encode(lowerCAmelCase__		)		)

			@slow
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
					    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
					    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
					)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[
					    8_7_1,
					    4_1_9,
					    3_5_8,
					    9_4_6,
					    9_9_1,
					    2_5_2_1,
					    4_5_2,
					    3_5_8,
					    1_3_5_7,
					    3_8_7,
					    7_7_5_1,
					    3_5_3_6,
					    1_1_2,
					    9_8_5,
					    4_5_6,
					    1_2_6,
					    8_6_5,
					    9_3_8,
					    5_4_0_0,
					    5_7_3_4,
					    4_5_8,
					    1_3_6_8,
					    4_6_7,
					    7_8_6,
					    2_4_6_2,
					    5_2_4_6,
					    1_1_5_9,
					    6_3_3,
					    8_6_5,
					    4_5_1_9,
					    4_5_7,
					    5_8_2,
					    8_5_2,
					    2_5_5_7,
					    4_2_7,
					    9_1_6,
					    5_0_8,
					    4_0_5,
					    3_4_3_2_4,
					    4_9_7,
					    3_9_1,
					    4_0_8,
					    1_1_3_4_2,
					    1_2_4_4,
					    3_8_5,
					    1_0_0,
					    9_3_8,
					    9_8_5,
					    4_5_6,
					    5_7_4,
					    3_6_2,
					    1_2_5_9_7,
					    3_2_0_0,
					    3_1_2_9,
					    1_1_7_2,
					]

					self.assertListEqual(lowerCAmelCase__							,  self.big_tokenizer.encode(lowerCAmelCase__		)		)

			@require_torch
			@slow
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					import torch

					from transformers import BertGenerationConfig, BertGenerationEncoder

					# Build sequence
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
					__SCREAMING_SNAKE_CASE :	int					 =	""" """.join(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.big_tokenizer.encode_plus(lowerCAmelCase__							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.big_tokenizer.batch_encode_plus(
					    [sequence + """ """ + sequence]							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	BertGenerationConfig()
					__SCREAMING_SNAKE_CASE :	Any					 =	BertGenerationEncoder(lowerCAmelCase__		)

					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

					with torch.no_grad():
							model(**lowerCAmelCase__		)
							model(**lowerCAmelCase__		)



			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{"""input_ids""": [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowerCAmelCase__							,  model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,  revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,  )