






"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.distilbert.modeling_tf_distilbert import (
			    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFDistilBertForMaskedLM,
			    TFDistilBertForMultipleChoice,
			    TFDistilBertForQuestionAnswering,
			    TFDistilBertForSequenceClassification,
			    TFDistilBertForTokenClassification,
			    TFDistilBertModel,
			)

class 						A_							:







				def __init__(							self							:						str						,    __lowerCamelCase							:						Optional[Any]						,    )				->					Union[str, Any]:
										__magic_name__            =     parent
										__magic_name__            =     1_3
										__magic_name__            =     7
										__magic_name__            =     True
										__magic_name__            =     True
										__magic_name__            =     False
										__magic_name__            =     True
										__magic_name__            =     9_9
										__magic_name__            =     3_2
										__magic_name__            =     2
										__magic_name__            =     4
										__magic_name__            =     3_7
										__magic_name__            =     "gelu"
										__magic_name__            =     0.1
										__magic_name__            =     0.1
										__magic_name__            =     5_1_2
										__magic_name__            =     1_6
										__magic_name__            =     2
										__magic_name__            =     0.02
										__magic_name__            =     3
										__magic_name__            =     4
										__magic_name__            =     None







				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size	)

										__magic_name__            =     None
										if self.use_input_mask:
																__magic_name__            =     random_attention_mask([self.batch_size, self.seq_length]	)

										__magic_name__            =     None
										__magic_name__            =     None
										__magic_name__            =     None
										if self.use_labels:
																__magic_name__            =     ids_tensor([self.batch_size]						,    self.type_sequence_label_size	)
																__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels	)
																__magic_name__            =     ids_tensor([self.batch_size]						,    self.num_choices	)

										__magic_name__            =     DistilBertConfig(
										    vocab_size=self.vocab_size						,    dim=self.hidden_size						,    n_layers=self.num_hidden_layers						,    n_heads=self.num_attention_heads						,    hidden_dim=self.intermediate_size						,    hidden_act=self.hidden_act						,    dropout=self.hidden_dropout_prob						,    attention_dropout=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    initializer_range=self.initializer_range						,    )

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						str	)				->					List[str]:
										__magic_name__            =     TFDistilBertModel(config=A_	)
										__magic_name__            =     {"input_ids": input_ids, "attention_mask": input_mask}

										__magic_name__            =     model(A_	)

										__magic_name__            =     [input_ids, input_mask]

										__magic_name__            =     model(A_	)

										self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)	)







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						str	)				->					Any:
										__magic_name__            =     TFDistilBertForMaskedLM(config=A_	)
										__magic_name__            =     {"input_ids": input_ids, "attention_mask": input_mask}
										__magic_name__            =     model(A_	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						str	)				->					Tuple:
										__magic_name__            =     TFDistilBertForQuestionAnswering(config=A_	)
										__magic_name__            =     {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										}
										__magic_name__            =     model(A_	)
										self.parent.assertEqual(result.start_logits.shape						,    (self.batch_size, self.seq_length)	)
										self.parent.assertEqual(result.end_logits.shape						,    (self.batch_size, self.seq_length)	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					Tuple:
										__magic_name__            =     self.num_labels
										__magic_name__            =     TFDistilBertForSequenceClassification(A_	)
										__magic_name__            =     {"input_ids": input_ids, "attention_mask": input_mask}
										__magic_name__            =     model(A_	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple	)				->					int:
										__magic_name__            =     self.num_choices
										__magic_name__            =     TFDistilBertForMultipleChoice(A_	)
										__magic_name__            =     tf.tile(tf.expand_dims(A_						,    1	)						,    (1, self.num_choices, 1)	)
										__magic_name__            =     tf.tile(tf.expand_dims(A_						,    1	)						,    (1, self.num_choices, 1)	)
										__magic_name__            =     {
										    "input_ids": multiple_choice_inputs_ids,
										    "attention_mask": multiple_choice_input_mask,
										}
										__magic_name__            =     model(A_	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_choices)	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						List[Any]	)				->					str:
										__magic_name__            =     self.num_labels
										__magic_name__            =     TFDistilBertForTokenClassification(A_	)
										__magic_name__            =     {"input_ids": input_ids, "attention_mask": input_mask}
										__magic_name__            =     model(A_	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__            =     self.prepare_config_and_inputs()
										((__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__))            =     config_and_inputs
										__magic_name__            =     {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict





@require_tf
class 						A_							(      _lowercase					,							_lowercase					,							unittest.TestCase  ):
				UpperCAmelCase__        =				(
				    (
				        TFDistilBertModel,
				        TFDistilBertForMaskedLM,
				        TFDistilBertForQuestionAnswering,
				        TFDistilBertForSequenceClassification,
				        TFDistilBertForTokenClassification,
				        TFDistilBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else None
				)
				UpperCAmelCase__        =				(
				    {
				        '''feature-extraction''': TFDistilBertModel,
				        '''fill-mask''': TFDistilBertForMaskedLM,
				        '''question-answering''': TFDistilBertForQuestionAnswering,
				        '''text-classification''': TFDistilBertForSequenceClassification,
				        '''token-classification''': TFDistilBertForTokenClassification,
				        '''zero-shot''': TFDistilBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						str	)				->					Union[str, Any]:
										__magic_name__            =     TFDistilBertModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=A_						,    dim=3_7	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Any	)				->					int:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_distilbert_model(*A_	)







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_distilbert_for_masked_lm(*A_	)







				def     _snake_case					(							self							:						str	)				->					Optional[int]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_distilbert_for_question_answering(*A_	)







				def     _snake_case					(							self							:						str	)				->					List[str]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_distilbert_for_sequence_classification(*A_	)







				def     _snake_case					(							self							:						Tuple	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_distilbert_for_multiple_choice(*A_	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Any:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_distilbert_for_token_classification(*A_	)







				@slow
				def     _snake_case					(							self							:						Tuple	)				->					Any:
										for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
																__magic_name__            =     TFDistilBertModel.from_pretrained(A_	)
																self.assertIsNotNone(A_	)





@require_tf
class 						A_							(      unittest.TestCase  ):







				@slow
				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										__magic_name__            =     TFDistilBertModel.from_pretrained("distilbert-base-uncased"	)
										__magic_name__            =     tf.constant([[0, 1, 2, 3, 4, 5]]	)
										__magic_name__            =     model(A_	)[0]

										__magic_name__            =     [1, 6, 7_6_8]
										self.assertEqual(output.shape						,    A_	)

										__magic_name__            =     tf.constant(
										    [
										        [
										            [0.1926_1885, -0.1373_2955, 0.411_9799],
										            [0.2215_0156, -0.0742_2661, 0.3903_7204],
										            [0.2275_6018, -0.089_6414, 0.370_1467],
										        ]
										    ]	)
										tf.debugging.assert_near(output[:, :3, :3]						,    A_						,    atol=1e-4	)











"""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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				42





class 						A_							(      snake_case_					,							snake_case_  ):
				UpperCAmelCase__        =				True







				@register_to_config
				def __init__(							self							:						Any						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Tuple[str] = ("DownEncoderBlock2D",)						,    __lowerCamelCase							:						Tuple[str] = ("UpDecoderBlock2D",)						,    __lowerCamelCase							:						Tuple[int] = (6_4,)						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						str = "silu"						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						float = 0.1_8215						,    )				->					Any:
										super().__init__()

										# pass init params to Encoder
										__magic_name__            =     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
										__magic_name__            =     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						,    )

										__magic_name__            =     nn.Convad(2 * latent_channels						,    2 * latent_channels						,    1	)
										__magic_name__            =     nn.Convad(__lowerCamelCase						,    __lowerCamelCase						,    1	)

										__magic_name__            =     False
										__magic_name__            =     False

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







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    (Encoder, Decoder)	):
																__magic_name__            =     value







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						bool = True	)				->					int:
										__magic_name__            =     use_tiling







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.enable_tiling(__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     True







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__            =     False







				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def     _snake_case					(							self							:						Optional[int]	)				->					Dict[str, AttentionProcessor]:
										__magic_name__            =     {}

										def fn_recursive_add_processors(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						Dict[str, AttentionProcessor]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						__magic_name__            =     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     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)				->					int:
										__magic_name__            =     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							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						List[str]	):
																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     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										self.set_attn_processor(AttnProcessor()	)







				@apply_forward_hook
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										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:
																__magic_name__            =     [self.encoder(__lowerCamelCase	) for x_slice in x.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self.encoder(__lowerCamelCase	)

										__magic_name__            =     self.quant_conv(__lowerCamelCase	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										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	)

										__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
										__magic_name__            =     self.decoder(__lowerCamelCase	)

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				@apply_forward_hook
				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_slicing and z.shape[0] > 1:
																__magic_name__            =     [self._decode(__lowerCamelCase	).sample for z_slice in z.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self._decode(__lowerCamelCase	).sample

										if not return_dict:
																return (decoded,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[2]						,    b.shape[2]						,    __lowerCamelCase	)
										for y in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[3]						,    b.shape[3]						,    __lowerCamelCase	)
										for x in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										__magic_name__            =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_latent_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_latent_min_size - blend_extent

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

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_sample_min_size * self.tile_overlap_factor	)
										__magic_name__            =     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.
										__magic_name__            =     []
										for i in range(0						,    z.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    z.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
																						__magic_name__            =     self.decoder(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																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:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[torch.Generator] = None						,    )				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     sample
										__magic_name__            =     self.encode(__lowerCamelCase	).latent_dist
										if sample_posterior:
																__magic_name__            =     posterior.sample(generator=__lowerCamelCase	)
										else:
																__magic_name__            =     posterior.mode()
										__magic_name__            =     self.decode(__lowerCamelCase	).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)



