



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase     :							List[str]  							=				get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[int]          =XLMRobertaTokenizer
		lowercase      :       Optional[Any]          =XLMRobertaTokenizerFast
		lowercase      :       Optional[Any]          =True
		lowercase      :       Union[str, Any]          =True






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[str]:
								'''simple docstring'''







								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase         =     XLMRobertaTokenizer(UpperCamelCase__				,		keep_accents=UpperCamelCase__      )
								tokenizer.save_pretrained(self.tmpdirname      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     "<pad>"
								UpperCAmelCase         =     1

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






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					int:
								'''simple docstring'''







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

								self.assertEqual(vocab_keys[0]				,		"<s>"      )
								self.assertEqual(vocab_keys[1]				,		"<pad>"      )
								self.assertEqual(vocab_keys[-1]				,		"<mask>"      )
								self.assertEqual(len(UpperCamelCase__      )				,		10_02      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					int:
								'''simple docstring'''







								self.assertEqual(self.get_tokenizer().vocab_size				,		10_02      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     XLMRobertaTokenizer(UpperCamelCase__				,		keep_accents=UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.tokenize("This is a test"      )
								self.assertListEqual(UpperCamelCase__				,		["▁This", "▁is", "▁a", "▁t", "est"]      )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,		)

								UpperCAmelCase         =     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        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",
								        ".",
								    ]				,		)
								UpperCAmelCase         =     tokenizer.convert_tokens_to_ids(UpperCamelCase__      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]				,		)

								UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(UpperCamelCase__      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        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>",
								        ".",
								    ]				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Optional[int]:
								'''simple docstring'''







								if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

								UpperCAmelCase         =     (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																				UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(UpperCamelCase__				,		**UpperCamelCase__      )
																				UpperCAmelCase         =     self.tokenizer_class.from_pretrained(UpperCamelCase__				,		**UpperCamelCase__      )

																				UpperCAmelCase         =     tempfile.mkdtemp()

																				UpperCAmelCase         =     tokenizer_r.save_pretrained(UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer_p.save_pretrained(UpperCamelCase__      )

																				# Checks it save with the same files + the tokenizer.json file for the fast one
																				self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )
																				UpperCAmelCase         =     tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f      )
																				self.assertSequenceEqual(UpperCamelCase__				,		UpperCamelCase__      )

																				# Checks everything loads correctly in the same way
																				UpperCAmelCase         =     tokenizer_r.from_pretrained(UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer_p.from_pretrained(UpperCamelCase__      )

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(UpperCamelCase__				,		UpperCamelCase__      )      )
																										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																				shutil.rmtree(UpperCamelCase__      )

																				# Save tokenizer rust, legacy_format=True
																				UpperCAmelCase         =     tempfile.mkdtemp()

																				UpperCAmelCase         =     tokenizer_r.save_pretrained(UpperCamelCase__				,		legacy_format=UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer_p.save_pretrained(UpperCamelCase__      )

																				# Checks it save with the same files
																				self.assertSequenceEqual(UpperCamelCase__				,		UpperCamelCase__      )

																				# Checks everything loads correctly in the same way
																				UpperCAmelCase         =     tokenizer_r.from_pretrained(UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer_p.from_pretrained(UpperCamelCase__      )

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(UpperCamelCase__				,		UpperCamelCase__      )      )

																				shutil.rmtree(UpperCamelCase__      )

																				# Save tokenizer rust, legacy_format=False
																				UpperCAmelCase         =     tempfile.mkdtemp()

																				UpperCAmelCase         =     tokenizer_r.save_pretrained(UpperCamelCase__				,		legacy_format=UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer_p.save_pretrained(UpperCamelCase__      )

																				# Checks it saved the tokenizer.json file
																				self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )

																				# Checks everything loads correctly in the same way
																				UpperCAmelCase         =     tokenizer_r.from_pretrained(UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer_p.from_pretrained(UpperCamelCase__      )

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(UpperCamelCase__				,		UpperCamelCase__      )      )

																				shutil.rmtree(UpperCamelCase__      )






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								with tempfile.NamedTemporaryFile() as f:
														shutil.copyfile(UpperCamelCase__				,		f.name      )
														UpperCAmelCase         =     XLMRobertaTokenizer(f.name				,		keep_accents=UpperCamelCase__      )
														UpperCAmelCase         =     pickle.dumps(UpperCamelCase__      )
								pickle.loads(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''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__      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "Hello World!"
								UpperCAmelCase         =     [0, 3_53_78, 66_61, 38, 2]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

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






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     [
								    0,
								    32_93,
								    83,
								    10,
								    45_52,
								    49_89,
								    79_86,
								    6_78,
								    10,
								    59_15,
								    1_11,
								    17_94_59,
								    12_48_50,
								    4,
								    60_44,
								    2_37,
								    12,
								    6,
								    5,
								    6,
								    4,
								    67_80,
								    7_05,
								    15,
								    13_88,
								    44,
								    3_78,
								    1_01_14,
								    7_11,
								    1_52,
								    20,
								    6,
								    5,
								    2_23_76,
								    6_42,
								    12_21,
								    1_51_90,
								    3_41_53,
								    4_50,
								    56_08,
								    9_59,
								    11_19,
								    5_77_02,
								    1_36,
								    1_86,
								    47,
								    10_98,
								    2_93_67,
								    47,
								    # 4426, # What fairseq tokenizes from "<unk>": "_<"
								    # 3678, # What fairseq tokenizes from "<unk>": "unk"
								    # 2740, # What fairseq tokenizes from "<unk>": ">"
								    3,  # What we tokenize from "<unk>": "<unk>"
								    6,  # Residue from the tokenization: an extra sentencepiece underline
								    4,
								    60_44,
								    2_37,
								    62_84,
								    5_09_01,
								    5_28,
								    31,
								    90,
								    34,
								    9_27,
								    2,
								]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

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







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], "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, 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], [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, 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="xlm-roberta-base"				,		revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"				,		)








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  __magic_name__   (							A__						):
		lowercase      :       "DiagonalGaussianDistribution"





class  __magic_name__   (							A__,  A__						):
		lowercase      :       Union[str, Any]          =True






		@register_to_config
		def __init__(						self      :     List[str]				,		UpperCamelCase__      :     int = 3				,		UpperCamelCase__      :     int = 3				,		UpperCamelCase__      :     Tuple[str] = ("DownEncoderBlock2D",)				,		UpperCamelCase__      :     Tuple[str] = ("UpDecoderBlock2D",)				,		UpperCamelCase__      :     Tuple[int] = (64,)				,		UpperCamelCase__      :     int = 1				,		UpperCamelCase__      :     str = "silu"				,		UpperCamelCase__      :     int = 4				,		UpperCamelCase__      :     int = 32				,		UpperCamelCase__      :     int = 32				,		UpperCamelCase__      :     float = 0.1_82_15				,		)				->					List[str]:
								'''simple docstring'''







								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase         =     Encoder(
								    in_channels=UpperCamelCase__				,		out_channels=UpperCamelCase__				,		down_block_types=UpperCamelCase__				,		block_out_channels=UpperCamelCase__				,		layers_per_block=UpperCamelCase__				,		act_fn=UpperCamelCase__				,		norm_num_groups=UpperCamelCase__				,		double_z=UpperCamelCase__				,		)

								# pass init params to Decoder
								UpperCAmelCase         =     Decoder(
								    in_channels=UpperCamelCase__				,		out_channels=UpperCamelCase__				,		up_block_types=UpperCamelCase__				,		block_out_channels=UpperCamelCase__				,		layers_per_block=UpperCamelCase__				,		norm_num_groups=UpperCamelCase__				,		act_fn=UpperCamelCase__				,		)

								UpperCAmelCase         =     nn.Convad(2 * latent_channels				,		2 * latent_channels				,		1      )
								UpperCAmelCase         =     nn.Convad(UpperCamelCase__				,		UpperCamelCase__				,		1      )

								UpperCAmelCase         =     False
								UpperCAmelCase         =     False

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     int=False      )				->					Optional[int]:
								'''simple docstring'''







								if isinstance(UpperCamelCase__				,		(Encoder, Decoder)      ):
														UpperCAmelCase         =     value






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     bool = True      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     use_tiling






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Tuple:
								'''simple docstring'''







								self.enable_tiling(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     True






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     False






		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict[str, AttentionProcessor]:
								'''simple docstring'''







								UpperCAmelCase         =     {}

								def fn_recursive_add_processors(UpperCamelCase__      :     str				,		UpperCamelCase__      :     torch.nn.Module				,		UpperCamelCase__      :     Dict[str, AttentionProcessor]      ):
														if hasattr(UpperCamelCase__				,		"set_processor"      ):
																				UpperCAmelCase         =     module.processor

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

														return processors

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

								return processors






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Union[AttentionProcessor, Dict[str, AttentionProcessor]]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     len(self.attn_processors.keys()      )

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

								def fn_recursive_attn_processor(UpperCamelCase__      :     str				,		UpperCamelCase__      :     torch.nn.Module				,		UpperCamelCase__      :     List[Any]      ):
														if hasattr(UpperCamelCase__				,		"set_processor"      ):
																				if not isinstance(UpperCamelCase__				,		UpperCamelCase__      ):
																										module.set_processor(UpperCamelCase__      )
																				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}'				,		UpperCamelCase__				,		UpperCamelCase__      )

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Dict:
								'''simple docstring'''







								self.set_attn_processor(AttnProcessor()      )






		@apply_forward_hook
		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					AutoencoderKLOutput:
								'''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(UpperCamelCase__				,		return_dict=UpperCamelCase__      )

								if self.use_slicing and x.shape[0] > 1:
														UpperCAmelCase         =     [self.encoder(UpperCamelCase__      ) for x_slice in x.split(1      )]
														UpperCAmelCase         =     torch.cat(UpperCamelCase__      )
								else:
														UpperCAmelCase         =     self.encoder(UpperCamelCase__      )

								UpperCAmelCase         =     self.quant_conv(UpperCamelCase__      )
								UpperCAmelCase         =     DiagonalGaussianDistribution(UpperCamelCase__      )

								if not return_dict:
														return (posterior,)

								return AutoencoderKLOutput(latent_dist=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					Union[DecoderOutput, torch.FloatTensor]:
								'''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(UpperCamelCase__				,		return_dict=UpperCamelCase__      )

								UpperCAmelCase         =     self.post_quant_conv(UpperCamelCase__      )
								UpperCAmelCase         =     self.decoder(UpperCamelCase__      )

								if not return_dict:
														return (dec,)

								return DecoderOutput(sample=UpperCamelCase__      )






		@apply_forward_hook
		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								if self.use_slicing and z.shape[0] > 1:
														UpperCAmelCase         =     [self._decode(UpperCamelCase__      ).sample for z_slice in z.split(1      )]
														UpperCAmelCase         =     torch.cat(UpperCamelCase__      )
								else:
														UpperCAmelCase         =     self._decode(UpperCamelCase__      ).sample

								if not return_dict:
														return (decoded,)

								return DecoderOutput(sample=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     int      )				->					List[str]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					AutoencoderKLOutput:
								'''simple docstring'''







								UpperCAmelCase         =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)      )
								UpperCAmelCase         =     int(self.tile_latent_min_size * self.tile_overlap_factor      )
								UpperCAmelCase         =     self.tile_latent_min_size - blend_extent

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

								UpperCAmelCase         =     torch.cat(UpperCamelCase__				,		dim=2      )
								UpperCAmelCase         =     DiagonalGaussianDistribution(UpperCamelCase__      )

								if not return_dict:
														return (posterior,)

								return AutoencoderKLOutput(latent_dist=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = True      )				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								UpperCAmelCase         =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)      )
								UpperCAmelCase         =     int(self.tile_sample_min_size * self.tile_overlap_factor      )
								UpperCAmelCase         =     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.
								UpperCAmelCase         =     []
								for i in range(0				,		z.shape[2]				,		UpperCamelCase__      ):
														UpperCAmelCase         =     []
														for j in range(0				,		z.shape[3]				,		UpperCamelCase__      ):
																				UpperCAmelCase         =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																				UpperCAmelCase         =     self.post_quant_conv(UpperCamelCase__      )
																				UpperCAmelCase         =     self.decoder(UpperCamelCase__      )
																				row.append(UpperCamelCase__      )
														rows.append(UpperCamelCase__      )
								UpperCAmelCase         =     []
								for i, row in enumerate(UpperCamelCase__      ):
														UpperCAmelCase         =     []
														for j, tile in enumerate(UpperCamelCase__      ):
																				# blend the above tile and the left tile
																				# to the current tile and add the current tile to the result row
																				if i > 0:
																										UpperCAmelCase         =     self.blend_v(rows[i - 1][j]				,		UpperCamelCase__				,		UpperCamelCase__      )
																				if j > 0:
																										UpperCAmelCase         =     self.blend_h(row[j - 1]				,		UpperCamelCase__				,		UpperCamelCase__      )
																				result_row.append(tile[:, :, :row_limit, :row_limit]      )
														result_rows.append(torch.cat(UpperCamelCase__				,		dim=3      )      )

								UpperCAmelCase         =     torch.cat(UpperCamelCase__				,		dim=2      )
								if not return_dict:
														return (dec,)

								return DecoderOutput(sample=UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     torch.FloatTensor				,		UpperCamelCase__      :     bool = False				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Optional[torch.Generator] = None				,		)				->					Union[DecoderOutput, torch.FloatTensor]:
								'''simple docstring'''







								UpperCAmelCase         =     sample
								UpperCAmelCase         =     self.encode(UpperCamelCase__      ).latent_dist
								if sample_posterior:
														UpperCAmelCase         =     posterior.sample(generator=UpperCamelCase__      )
								else:
														UpperCAmelCase         =     posterior.mode()
								UpperCAmelCase         =     self.decode(UpperCamelCase__      ).sample

								if not return_dict:
														return (dec,)

								return DecoderOutput(sample=UpperCamelCase__      )



