

'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
      import tensorflow as tf

      from transformers import TFResNetForImageClassification, TFResNetModel
      from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor


class 					A__      :



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=3		,				UpperCamelCase__=32		,				UpperCamelCase__=3		,				UpperCamelCase__=10		,				UpperCamelCase__=[10, 20, 30, 40]		,				UpperCamelCase__=[1, 1, 2, 1]		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__="relu"		,				UpperCamelCase__=3		,				UpperCamelCase__=None		,				)						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      parent
           A_						=      batch_size
           A_						=      image_size
           A_						=      num_channels
           A_						=      embeddings_size
           A_						=      hidden_sizes
           A_						=      depths
           A_						=      is_training
           A_						=      use_labels
           A_						=      hidden_act
           A_						=      num_labels
           A_						=      scope
           A_						=      len(UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           A_						=      None
           if self.use_labels:
                  A_						=      ids_tensor([self.batch_size]		,				self.num_labels       )

           A_						=      self.get_config()

           return config, pixel_values, labels



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           return ResNetConfig(
               num_channels=self.num_channels		,				embeddings_size=self.embeddings_size		,				hidden_sizes=self.hidden_sizes		,				depths=self.depths		,				hidden_act=self.hidden_act		,				num_labels=self.num_labels		,				image_size=self.image_size		,				)



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      TFResNetModel(config=UpperCamelCase__       )
           A_						=      model(UpperCamelCase__       )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape		,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,				)



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





           '''simple docstring'''
           A_						=      self.num_labels
           A_						=      TFResNetForImageClassification(UpperCamelCase__       )
           A_						=      model(UpperCamelCase__		,				labels=UpperCamelCase__       )
           self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)       )







    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      self.prepare_config_and_inputs()
           A_					,			A_					,			A_						=      config_and_inputs
           A_						=      {"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_tf
class 					A__      (      _snake_case						,						_snake_case						,						unittest.TestCase			):
    lowercase						=		(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
    lowercase						=		(
        {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
        if is_tf_available()
        else {}
    )

    lowercase						=		False
    lowercase						=		False
    lowercase						=		False
    lowercase						=		False
    lowercase						=		False



    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      TFResNetModelTester(self       )
           A_						=      ConfigTester(self		,				config_class=UpperCamelCase__		,				has_text_modality=UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           return



    @unittest.skip(reason="""ResNet does not use inputs_embeds"""       )
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""ResNet does not support input and output embeddings"""       )
    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           pass



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           A_					,			A_						=      self.model_tester.prepare_config_and_inputs_for_common()

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

                  A_						=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,				UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           def check_hidden_states_output(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ):
                  A_						=      model_class(UpperCamelCase__       )
                  A_						=      model(**self._prepare_for_class(UpperCamelCase__		,				UpperCamelCase__       )       )

                  A_						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  A_						=      self.model_tester.num_stages
                  self.assertEqual(len(UpperCamelCase__       )		,				expected_num_stages + 1       )

                  # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:]       )		,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,				)

           A_					,			A_						=      self.model_tester.prepare_config_and_inputs_for_common()
           A_						=      ["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         A_						=      layer_type
                         A_						=      True
                         check_hidden_states_output(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )

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

                         check_hidden_states_output(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )







    @slow
    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						=      TFResNetModel.from_pretrained(UpperCamelCase__       )
                  self.assertIsNotNone(UpperCamelCase__       )

def       UpperCAmelCase__    (      )			->			str:
       A_						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       return image







@require_tf
@require_vision
class 					A__      (      unittest.TestCase			):



    @cached_property
    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
               if is_vision_available()
               else None
           )







    @slow
    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

           A_						=      self.default_image_processor
           A_						=      prepare_img()
           A_						=      image_processor(images=UpperCamelCase__		,				return_tensors="""tf"""       )

           # forward pass
           A_						=      model(**UpperCamelCase__       )

           # verify the logits
           A_						=      tf.TensorShape((1, 1000)       )
           self.assertEqual(outputs.logits.shape		,				UpperCamelCase__       )

           A_						=      tf.constant([-11.1069, -9.7877, -8.3777]       )

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,				UpperCamelCase__		,				atol=1e-4       )       )



'''simple docstring'''







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__      (      _snake_case			):
    lowercase						=		42


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__=3		,				UpperCamelCase__=3		,				UpperCamelCase__=("DownEncoderBlock2D",)		,				UpperCamelCase__=(64,)		,				UpperCamelCase__=2		,				UpperCamelCase__=32		,				UpperCamelCase__="silu"		,				UpperCamelCase__=True		,				)						->      Union[str, Any]:





           '''simple docstring'''
           super().__init__()
           A_						=      layers_per_block

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

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

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

                  A_						=      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
           A_						=      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
           A_						=      nn.GroupNorm(num_channels=block_out_channels[-1]		,				num_groups=UpperCamelCase__		,				eps=1e-6       )
           A_						=      nn.SiLU()

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

           A_						=      False







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      x
           A_						=      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:
                                A_						=      torch.utils.checkpoint.checkpoint(
                                    create_custom_forward(UpperCamelCase__       )		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                         # middle
                         A_						=      torch.utils.checkpoint.checkpoint(
                             create_custom_forward(self.mid_block       )		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                  else:
                         for down_block in self.down_blocks:
                                A_						=      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__       )		,				UpperCamelCase__       )
                         # middle
                         A_						=      torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block       )		,				UpperCamelCase__       )

           else:
                  # down
                  for down_block in self.down_blocks:
                         A_						=      down_block(UpperCamelCase__       )

                  # middle
                  A_						=      self.mid_block(UpperCamelCase__       )

           # post-process
           A_						=      self.conv_norm_out(UpperCamelCase__       )
           A_						=      self.conv_act(UpperCamelCase__       )
           A_						=      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"		,				)						->      List[Any]:





           '''simple docstring'''
           super().__init__()
           A_						=      layers_per_block

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

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

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

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

                  A_						=      i == len(UpperCamelCase__       ) - 1

                  A_						=      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__       )
                  A_						=      output_channel

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

           A_						=      False







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      z
           A_						=      self.conv_in(UpperCamelCase__       )

           A_						=      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
                         A_						=      torch.utils.checkpoint.checkpoint(
                             create_custom_forward(self.mid_block       )		,				UpperCamelCase__		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                         A_						=      sample.to(UpperCamelCase__       )

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

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

                  # up
                  for up_block in self.up_blocks:
                         A_						=      up_block(UpperCamelCase__		,				UpperCamelCase__       )

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

           return sample


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__="random"		,				UpperCamelCase__=False		,				UpperCamelCase__=True       )						->      str:





           '''simple docstring'''
           super().__init__()
           A_						=      n_e
           A_						=      vq_embed_dim
           A_						=      beta
           A_						=      legacy

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

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

           A_						=      sane_index_shape



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[Any]:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Dict:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           # reshape z -> (batch, height, width, channel) and flatten
           A_						=      z.permute(0		,				2		,				3		,				1       ).contiguous()
           A_						=      z.view(-1		,				self.vq_embed_dim       )

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

           A_						=      self.embedding(UpperCamelCase__       ).view(z.shape       )
           A_						=      None
           A_						=      None

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

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

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

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

           if self.sane_index_shape:
                  A_						=      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__       )						->      Optional[int]:





           '''simple docstring'''
           # shape specifying (batch, height, width, channel)
           if self.remap is not None:
                  A_						=      indices.reshape(shape[0]		,				-1       )  # add batch axis
                  A_						=      self.unmap_to_all(UpperCamelCase__       )
                  A_						=      indices.reshape(-1       )  # flatten again

           # get quantized latent vectors
           A_						=      self.embedding(UpperCamelCase__       )

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

           return z_q


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=False       )						->      Dict:





           '''simple docstring'''
           A_						=      parameters
           A_					,			A_						=      torch.chunk(UpperCamelCase__		,				2		,				dim=1       )
           A_						=      torch.clamp(self.logvar		,				-30.0		,				20.0       )
           A_						=      deterministic
           A_						=      torch.exp(0.5 * self.logvar       )
           A_						=      torch.exp(self.logvar       )
           if self.deterministic:
                  A_						=      A_						=      torch.zeros_like(
                      self.mean		,				device=self.parameters.device		,				dtype=self.parameters.dtype       )



    def 					snake_case_			(					self		,				UpperCamelCase__ = None       )						->      torch.FloatTensor:





           '''simple docstring'''
           # make sure sample is on the same device as the parameters and has same dtype
           A_						=      randn_tensor(
               self.mean.shape		,				generator=UpperCamelCase__		,				device=self.parameters.device		,				dtype=self.parameters.dtype       )
           A_						=      self.mean + self.std * sample
           return x



    def 					snake_case_			(					self		,				UpperCamelCase__=None       )						->      int:





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



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=[1, 2, 3]       )						->      Optional[Any]:





           '''simple docstring'''
           if self.deterministic:
                  return torch.Tensor([0.0]       )
           A_						=      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       )						->      List[Any]:





           '''simple docstring'''
           return self.mean
