





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
							    VideoMAEForPreTraining,
							    VideoMAEForVideoClassification,
							    VideoMAEModel,
							)
							from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from transformers import VideoMAEImageProcessor

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=10   , UpperCAmelCase_=3   , UpperCAmelCase_=2   , UpperCAmelCase_=2   , UpperCAmelCase_=2   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=32   , UpperCAmelCase_=5   , UpperCAmelCase_=4   , UpperCAmelCase_=37   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=10   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=0.9   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =image_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =tubelet_size
								lowerCamelCase	 =num_frames
								lowerCamelCase	 =is_training
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =mask_ratio
								lowerCamelCase	 =scope

								# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
								lowerCamelCase	 =(image_size // patch_size) ** 2
								lowerCamelCase	 =(num_frames // tubelet_size) * self.num_patches_per_frame

								# use this variable to define bool_masked_pos
								lowerCamelCase	 =int(mask_ratio * self.seq_length							)


				def _snake_case       (		self							):
								lowerCamelCase	 =floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

								lowerCamelCase	 =None
								if self.use_labels:
												lowerCamelCase	 =ids_tensor([self.batch_size]   , self.type_sequence_label_size							)

								lowerCamelCase	 =self.get_config()

								return config, pixel_values, labels


				def _snake_case       (		self							):
								return VideoMAEConfig(
								    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , num_frames=self.num_frames   , tubelet_size=self.tubelet_size   , hidden_size=self.hidden_size   , num_hidden_layers=self.num_hidden_layers   , num_attention_heads=self.num_attention_heads   , intermediate_size=self.intermediate_size   , hidden_act=self.hidden_act   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , is_decoder=__lowerCAmelCase   , initializer_range=self.initializer_range   , )


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =VideoMAEModel(config=__lowerCAmelCase							)
								model.to(__lowerCAmelCase							)
								model.eval()
								lowerCamelCase	 =model(__lowerCAmelCase							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =VideoMAEForPreTraining(__lowerCAmelCase							)
								model.to(__lowerCAmelCase							)
								model.eval()
								# important: each video needs to have the same number of masked patches
								# hence we define a single mask, which we then repeat for each example in the batch
								lowerCamelCase	 =torch.ones((self.num_masks,)							)
								lowerCamelCase	 =torch.cat([mask, torch.zeros(self.seq_length - mask.size(0							)							)]							)
								lowerCamelCase	 =mask.expand(self.batch_size   , -1							).bool()

								lowerCamelCase	 =model(__lowerCAmelCase   , __lowerCAmelCase							)
								# model only returns predictions for masked patches
								lowerCamelCase	 =mask.sum().item()
								lowerCamelCase	 =3 * self.tubelet_size * self.patch_size**2
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, num_masked_patches, decoder_num_labels)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(
				    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
				)
				__A      =				(
				    {"""feature-extraction""": VideoMAEModel, """video-classification""": VideoMAEForVideoClassification}
				    if is_torch_available()
				    else {}
				)

				__A      =				False
				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =VideoMAEModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=__lowerCAmelCase   , has_text_modality=__lowerCAmelCase   , hidden_size=37							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase	 =copy.deepcopy(__lowerCAmelCase							)

								if model_class == VideoMAEForPreTraining:
												# important: each video needs to have the same number of masked patches
												# hence we define a single mask, which we then repeat for each example in the batch
												lowerCamelCase	 =torch.ones((self.model_tester.num_masks,)							)
												lowerCamelCase	 =torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0							)							)]							)
												lowerCamelCase	 =mask.expand(self.model_tester.batch_size   , -1							).bool()
												lowerCamelCase	 =bool_masked_pos.to(__lowerCAmelCase							)

								if return_labels:
												if model_class in [
												    *get_values(__lowerCAmelCase							),
												]:
																lowerCamelCase	 =torch.zeros(
																    self.model_tester.batch_size   , dtype=torch.long   , device=__lowerCAmelCase							)

								return inputs_dict


				def _snake_case       (		self							):
								self.config_tester.run_common_tests()


				@unittest.skip(reason="""VideoMAE does not use inputs_embeds"""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowerCamelCase	 =model_class(__lowerCAmelCase							)
												self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)							)
												lowerCamelCase	 =model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__lowerCAmelCase   , nn.Linear							)							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowerCamelCase	 =model_class(__lowerCAmelCase							)
												lowerCamelCase	 =inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowerCamelCase	 =[*signature.parameters.keys()]

												lowerCamelCase	 =["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   , __lowerCAmelCase							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase							)


				@slow
				def _snake_case       (		self							):







								for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase	 =VideoMAEModel.from_pretrained(__lowerCAmelCase							)
												self.assertIsNotNone(__lowerCAmelCase							)


				def _snake_case       (		self							):







								if not self.has_attentions:
												pass

								else:
												lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase	 =True

												for model_class in self.all_model_classes:
																lowerCamelCase	 =self.model_tester.seq_length - self.model_tester.num_masks
																lowerCamelCase	 =(
																    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																)

																lowerCamelCase	 =True
																lowerCamelCase	 =False
																lowerCamelCase	 =True
																lowerCamelCase	 =model_class(__lowerCAmelCase							)
																model.to(__lowerCAmelCase							)
																model.eval()
																with torch.no_grad():
																				lowerCamelCase	 =model(**self._prepare_for_class(__lowerCAmelCase   , __lowerCAmelCase							)							)
																lowerCamelCase	 =outputs.attentions
																self.assertEqual(len(__lowerCAmelCase							)   , self.model_tester.num_hidden_layers							)

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																lowerCamelCase	 =True
																lowerCamelCase	 =model_class(__lowerCAmelCase							)
																model.to(__lowerCAmelCase							)
																model.eval()
																with torch.no_grad():
																				lowerCamelCase	 =model(**self._prepare_for_class(__lowerCAmelCase   , __lowerCAmelCase							)							)
																lowerCamelCase	 =outputs.attentions
																self.assertEqual(len(__lowerCAmelCase							)   , self.model_tester.num_hidden_layers							)

																self.assertListEqual(
																    list(attentions[0].shape[-3:]							)   , [self.model_tester.num_attention_heads, seq_len, seq_len]   , )
																lowerCamelCase	 =len(__lowerCAmelCase							)

																# Check attention is always last and order is fine
																lowerCamelCase	 =True
																lowerCamelCase	 =True
																lowerCamelCase	 =model_class(__lowerCAmelCase							)
																model.to(__lowerCAmelCase							)
																model.eval()
																with torch.no_grad():
																				lowerCamelCase	 =model(**self._prepare_for_class(__lowerCAmelCase   , __lowerCAmelCase							)							)

																self.assertEqual(out_len + 1   , len(__lowerCAmelCase							)							)

																lowerCamelCase	 =outputs.attentions

																self.assertEqual(len(__lowerCAmelCase							)   , self.model_tester.num_hidden_layers							)
																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]							)   , [self.model_tester.num_attention_heads, seq_len, seq_len]   , )


				def _snake_case       (		self							):







								def check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
												lowerCamelCase	 =model_class(__lowerCAmelCase							)
												model.to(__lowerCAmelCase							)
												model.eval()

												with torch.no_grad():
																lowerCamelCase	 =model(**self._prepare_for_class(__lowerCAmelCase   , __lowerCAmelCase							)							)

												lowerCamelCase	 =outputs.hidden_states
												lowerCamelCase	 =self.model_tester.num_hidden_layers + 1
												self.assertEqual(len(__lowerCAmelCase							)   , __lowerCAmelCase							)

												lowerCamelCase	 =self.model_tester.seq_length - self.model_tester.num_masks
												lowerCamelCase	 =num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]							)   , [seq_length, self.model_tester.hidden_size]   , )

								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowerCamelCase	 =True
												check_hidden_states_output(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowerCamelCase	 =True

												check_hidden_states_output(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase							)


				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
				def _snake_case       (		self							):
								pass





def      _lowercase					(						)						->  int:
				lowerCamelCase	 =hf_hub_download(
				    repo_id="""hf-internal-testing/spaghetti-video"""			,      filename="""eating_spaghetti.npy"""			,      repo_type="""dataset"""				)
				lowerCamelCase	 =np.load(__snake_case				)
				return list(__snake_case				)

@require_torch
@require_vision
class       __A     (						unittest.TestCase    ):


				@cached_property
				def _snake_case       (		self							):
								return (
								    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   , image_std=[0.5, 0.5, 0.5]							)
								    if is_vision_available()
								    else None
								)


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics"""							).to(
								    __lowerCAmelCase							)

								lowerCamelCase	 =self.default_image_processor
								lowerCamelCase	 =prepare_video()
								lowerCamelCase	 =image_processor(__lowerCAmelCase   , return_tensors="""pt"""							).to(__lowerCAmelCase							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**__lowerCAmelCase							)

								# verify the logits
								lowerCamelCase	 =torch.Size((1, 400)							)
								self.assertEqual(outputs.logits.shape   , __lowerCAmelCase							)

								lowerCamelCase	 =torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]							).to(__lowerCAmelCase							)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]   , __lowerCAmelCase   , atol=1E-4							)							)


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""							).to(__lowerCAmelCase							)

								lowerCamelCase	 =self.default_image_processor
								lowerCamelCase	 =prepare_video()
								lowerCamelCase	 =image_processor(__lowerCAmelCase   , return_tensors="""pt"""							).to(__lowerCAmelCase							)

								# add boolean mask, indicating which patches to mask
								lowerCamelCase	 =hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""   , filename="""bool_masked_pos.pt"""							)
								lowerCamelCase	 =torch.load(__lowerCAmelCase							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**__lowerCAmelCase							)

								# verify the logits
								lowerCamelCase	 =torch.Size([1, 1408, 1536]							)
								lowerCamelCase	 =torch.tensor(
								    [[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   , device=__lowerCAmelCase							)
								self.assertEqual(outputs.logits.shape   , __lowerCAmelCase							)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   , __lowerCAmelCase   , atol=1E-4							)							)

								# verify the loss (`config.norm_pix_loss` = `True`)
								lowerCamelCase	 =torch.tensor([0.5_1_4_2]   , device=__lowerCAmelCase							)
								self.assertTrue(torch.allclose(outputs.loss   , __lowerCAmelCase   , atol=1E-4							)							)

								# verify the loss (`config.norm_pix_loss` = `False`)
								lowerCamelCase	 =VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""   , norm_pix_loss=__lowerCAmelCase							).to(
								    __lowerCAmelCase							)

								with torch.no_grad():
												lowerCamelCase	 =model(**__lowerCAmelCase							)

								lowerCamelCase	 =torch.tensor(torch.tensor([0.6_4_6_9]							)   , device=__lowerCAmelCase							)
								self.assertTrue(torch.allclose(outputs.loss   , __lowerCAmelCase   , atol=1E-4							)							)












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       __A     (						a    ):
				__A      =				42




class       __A     (						nn.Module    ):


				def __init__(		self   , UpperCAmelCase_=3   , UpperCAmelCase_=3   , UpperCAmelCase_=("DownEncoderBlock2D",)   , UpperCAmelCase_=(64,)   , UpperCAmelCase_=2   , UpperCAmelCase_=32   , UpperCAmelCase_="silu"   , UpperCAmelCase_=True   , ):
								super().__init__()
								lowerCamelCase	 =layers_per_block

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

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

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

												lowerCamelCase	 =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
								lowerCamelCase	 =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
								lowerCamelCase	 =nn.GroupNorm(num_channels=block_out_channels[-1]   , num_groups=UpperCAmelCase_   , eps=1E-6							)
								lowerCamelCase	 =nn.SiLU()

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

								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =x
								lowerCamelCase	 =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:
																				lowerCamelCase	 =torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(UpperCAmelCase_							)   , UpperCAmelCase_   , use_reentrant=UpperCAmelCase_							)
																# middle
																lowerCamelCase	 =torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block							)   , UpperCAmelCase_   , use_reentrant=UpperCAmelCase_							)
												else:
																for down_block in self.down_blocks:
																				lowerCamelCase	 =torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase_							)   , UpperCAmelCase_							)
																# middle
																lowerCamelCase	 =torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)   , UpperCAmelCase_							)

								else:
												# down
												for down_block in self.down_blocks:
																lowerCamelCase	 =down_block(UpperCAmelCase_							)

												# middle
												lowerCamelCase	 =self.mid_block(UpperCAmelCase_							)

								# post-process
								lowerCamelCase	 =self.conv_norm_out(UpperCAmelCase_							)
								lowerCamelCase	 =self.conv_act(UpperCAmelCase_							)
								lowerCamelCase	 =self.conv_out(UpperCAmelCase_							)

								return sample




class       __A     (						nn.Module    ):


				def __init__(		self   , UpperCAmelCase_=3   , UpperCAmelCase_=3   , UpperCAmelCase_=("UpDecoderBlock2D",)   , UpperCAmelCase_=(64,)   , UpperCAmelCase_=2   , UpperCAmelCase_=32   , UpperCAmelCase_="silu"   , UpperCAmelCase_="group"   , ):
								super().__init__()
								lowerCamelCase	 =layers_per_block

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

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

								lowerCamelCase	 =in_channels if norm_type == """spatial""" else None

								# mid
								lowerCamelCase	 =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
								lowerCamelCase	 =list(reversed(UpperCAmelCase_							)							)
								lowerCamelCase	 =reversed_block_out_channels[0]
								for i, up_block_type in enumerate(UpperCAmelCase_							):
												lowerCamelCase	 =output_channel
												lowerCamelCase	 =reversed_block_out_channels[i]

												lowerCamelCase	 =i == len(UpperCAmelCase_							) - 1

												lowerCamelCase	 =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_							)
												lowerCamelCase	 =output_channel

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

								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=None							):
								lowerCamelCase	 =z
								lowerCamelCase	 =self.conv_in(UpperCAmelCase_							)

								lowerCamelCase	 =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
																lowerCamelCase	 =torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block							)   , UpperCAmelCase_   , UpperCAmelCase_   , use_reentrant=UpperCAmelCase_							)
																lowerCamelCase	 =sample.to(UpperCAmelCase_							)

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

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

												# up
												for up_block in self.up_blocks:
																lowerCamelCase	 =up_block(UpperCAmelCase_   , UpperCAmelCase_							)

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

								return sample




class       __A     (						nn.Module    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , UpperCAmelCase_="random"   , UpperCAmelCase_=False   , UpperCAmelCase_=True							):
								super().__init__()
								lowerCamelCase	 =n_e
								lowerCamelCase	 =vq_embed_dim
								lowerCamelCase	 =beta
								lowerCamelCase	 =legacy

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

								lowerCamelCase	 =remap
								if self.remap is not None:
												self.register_buffer("""used"""   , torch.tensor(np.load(self.remap							)							)							)
												lowerCamelCase	 =self.used.shape[0]
												lowerCamelCase	 =unknown_index  # "random" or "extra" or integer
												if self.unknown_index == "extra":
																lowerCamelCase	 =self.re_embed
																lowerCamelCase	 =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:
												lowerCamelCase	 =n_e

								lowerCamelCase	 =sane_index_shape


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


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


				def _snake_case       (		self   , UpperCAmelCase_							):
								# reshape z -> (batch, height, width, channel) and flatten
								lowerCamelCase	 =z.permute(0   , 2   , 3   , 1							).contiguous()
								lowerCamelCase	 =z.view(-1   , self.vq_embed_dim							)

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

								lowerCamelCase	 =self.embedding(UpperCAmelCase_							).view(z.shape							)
								lowerCamelCase	 =None
								lowerCamelCase	 =None

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

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

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

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

								if self.sane_index_shape:
												lowerCamelCase	 =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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								# shape specifying (batch, height, width, channel)
								if self.remap is not None:
												lowerCamelCase	 =indices.reshape(shape[0]   , -1							)  # add batch axis
												lowerCamelCase	 =self.unmap_to_all(UpperCAmelCase_							)
												lowerCamelCase	 =indices.reshape(-1							)  # flatten again

								# get quantized latent vectors
								lowerCamelCase	 =self.embedding(UpperCAmelCase_							)

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

								return z_q




class       __A     (						a    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase	 =parameters
								lowerCamelCase ,  lowerCamelCase	 =torch.chunk(UpperCAmelCase_   , 2   , dim=1							)
								lowerCamelCase	 =torch.clamp(self.logvar   , -3_0.0   , 2_0.0							)
								lowerCamelCase	 =deterministic
								lowerCamelCase	 =torch.exp(0.5 * self.logvar							)
								lowerCamelCase	 =torch.exp(self.logvar							)
								if self.deterministic:
												lowerCamelCase	 =lowerCamelCase	 =torch.zeros_like(
												    self.mean   , device=self.parameters.device   , dtype=self.parameters.dtype							)


				def _snake_case       (		self   , UpperCAmelCase_ = None							):
								# make sure sample is on the same device as the parameters and has same dtype
								lowerCamelCase	 =randn_tensor(
								    self.mean.shape   , generator=UpperCAmelCase_   , device=self.parameters.device   , dtype=self.parameters.dtype							)
								lowerCamelCase	 =self.mean + self.std * sample
								return x


				def _snake_case       (		self   , UpperCAmelCase_=None							):
								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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=[1, 2, 3]							):
								if self.deterministic:
												return torch.Tensor([0.0]							)
								lowerCamelCase	 =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 _snake_case       (		self							):
								return self.mean





