code: 
  
  import gc
  import os
  import tempfile
  import unittest
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
  from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
  from diffusers.models.attention_processor import (
      Attention,
      AttnProcessor,
      AttnProcessor2_0,
      LoRAAttnProcessor,
      LoRAAttnProcessor2_0,
      LoRAXFormersAttnProcessor,
      XFormersAttnProcessor,
  )
  from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
  
  
  
  
  def 						A_			(  lowercase_  )			->						Tuple:
         _snake_case      :							Optional[int]     				=						{}
         for name in unet.attn_processors.keys():
                _snake_case      :							Union[str, Any]     				=						None if name.endswith('''attn1.processor'''  ) else unet.config.cross_attention_dim
                if name.startswith('''mid_block'''  ):
                       _snake_case      :							Tuple     				=						unet.config.block_out_channels[-1]
                elif name.startswith('''up_blocks'''  ):
                       _snake_case      :							List[str]     				=						int(name[len('''up_blocks.'''  )]  )
                       _snake_case      :							List[Any]     				=						list(reversed(unet.config.block_out_channels  )  )[block_id]
                elif name.startswith('''down_blocks'''  ):
                       _snake_case      :							Dict     				=						int(name[len('''down_blocks.'''  )]  )
                       _snake_case      :							List[str]     				=						unet.config.block_out_channels[block_id]
                _snake_case      :							Optional[int]     				=						(
                    LoRAAttnProcessor2_0 if hasattr(lowercase_      ,		'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
                )
                _snake_case      :							List[Any]     				=						lora_attn_processor_class(
                    hidden_size=lowercase_      ,		cross_attention_dim=lowercase_  )
         _snake_case      :							Optional[int]     				=						AttnProcsLayers(lowercase_  )
         return lora_attn_procs, unet_lora_layers
  
  
  
  
  def 						A_			(  lowercase_  )			->						Union[str, Any]:
         _snake_case      :							Optional[int]     				=						{}
         _snake_case      :							int     				=						(
             LoRAAttnProcessor2_0 if hasattr(lowercase_      ,		'''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
         )
         for name, module in text_encoder.named_modules():
                if name.endswith(lowercase_  ):
                       _snake_case      :							Dict     				=						lora_attn_processor_class(
                           hidden_size=module.out_proj.out_features      ,		cross_attention_dim=lowercase_  )
         return text_lora_attn_procs
  
  
  
  
  def 						A_			(  lowercase_  )			->						str:
         _snake_case      :							str     				=						create_text_encoder_lora_attn_procs(lowercase_  )
         _snake_case      :							List[str]     				=						AttnProcsLayers(lowercase_  )
         return text_encoder_lora_layers
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=False  )			->						int:
         for _, attn_proc in text_lora_attn_procs.items():
                # set up.weights
                for layer_name, layer_module in attn_proc.named_modules():
                       if layer_name.endswith('''_lora'''  ):
                              _snake_case      :							int     				=						(
                                  torch.randn_like(layer_module.up.weight  )
                                  if randn_weight
                                  else torch.zeros_like(layer_module.up.weight  )
                              )
                              _snake_case      :							str     				=						torch.nn.Parameter(lowercase_  )
  
  class 				A    (unittest.TestCase				):
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               torch.manual_seed(0					)
               _snake_case      :							Optional[Any]     				=						UNetaDConditionModel(
                   block_out_channels=(32, 64)     ,  layers_per_block=2     ,  sample_size=32     ,  in_channels=4     ,  out_channels=4     ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,  cross_attention_dim=32     ,  )
               _snake_case      :							List[Any]     				=						DDIMScheduler(
                   beta_start=0.0_0085     ,  beta_end=0.012     ,  beta_schedule='''scaled_linear'''     ,  clip_sample=lowercase_     ,  set_alpha_to_one=lowercase_     ,  steps_offset=1     ,  )
               torch.manual_seed(0					)
               _snake_case      :							Tuple     				=						AutoencoderKL(
                   block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,  latent_channels=4     ,  )
               _snake_case      :							Optional[Any]     				=						CLIPTextConfig(
                   bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=32     ,  intermediate_size=37     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1000     ,  )
               _snake_case      :							Optional[int]     				=						CLIPTextModel(lowercase_					)
               _snake_case      :							Optional[Any]     				=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
  
               _snake_case      :							Dict     				=						create_unet_lora_layers(lowercase_					)
               _snake_case      :							str     				=						create_text_encoder_lora_layers(lowercase_					)
  
               _snake_case      :							List[Any]     				=						{
                   '''unet''': unet,
                   '''scheduler''': scheduler,
                   '''vae''': vae,
                   '''text_encoder''': text_encoder,
                   '''tokenizer''': tokenizer,
                   '''safety_checker''': None,
                   '''feature_extractor''': None,
               }
               _snake_case      :							int     				=						{
                   '''unet_lora_layers''': unet_lora_layers,
                   '''text_encoder_lora_layers''': text_encoder_lora_layers,
                   '''unet_lora_attn_procs''': unet_lora_attn_procs,
               }
               return pipeline_components, lora_components
  
  
  
        def    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						1
               _snake_case      :							int     				=						10
               _snake_case      :							List[Any]     				=						4
               _snake_case      :							List[str]     				=						(32, 32)
  
               _snake_case      :							Dict     				=						torch.manual_seed(0					)
               _snake_case      :							Union[str, Any]     				=						floats_tensor((batch_size, num_channels) + sizes					)
               _snake_case      :							Any     				=						torch.randint(1     ,  lowercase_     ,  size=(batch_size, sequence_length)     ,  generator=lowercase_					)
  
               _snake_case      :							List[Any]     				=						{
                   '''prompt''': '''A painting of a squirrel eating a burger''',
                   '''generator''': generator,
                   '''num_inference_steps''': 2,
                   '''guidance_scale''': 6.0,
                   '''output_type''': '''numpy''',
               }
  
               return noise, input_ids, pipeline_inputs
  
               # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						77
  
               _snake_case      :							Tuple     				=						torch.randint(2     ,  56     ,  size=(1, max_seq_length)     ,  generator=torch.manual_seed(0					)					)
  
               _snake_case      :							Optional[int]     				=						{}
               _snake_case      :							Optional[int]     				=						inputs
               return prepared_inputs
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						self.get_dummy_components()
               LoraLoaderMixin.save_lora_weights(
                   save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
               self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.get_dummy_components()
               _snake_case      :							Optional[Any]     				=						StableDiffusionPipeline(**lowercase_					)
               _snake_case      :							Optional[Any]     				=						sd_pipe.to(lowercase_					)
               sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
               _snake_case      :							Union[str, Any]     				=						self.get_dummy_inputs()
  
               _snake_case      :							Optional[int]     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							str     				=						original_images[0, -3:, -3:, -1]
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      LoraLoaderMixin.save_lora_weights(
                          save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
                      self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                      sd_pipe.load_lora_weights(lowercase_					)
  
               _snake_case      :							int     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Optional[int]     				=						lora_images[0, -3:, -3:, -1]
  
               # Outputs shouldn't match.
               self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						self.get_dummy_components()
               _snake_case      :							Optional[int]     				=						StableDiffusionPipeline(**lowercase_					)
               _snake_case      :							str     				=						sd_pipe.to(lowercase_					)
               sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
               _snake_case      :							Any     				=						self.get_dummy_inputs()
  
               _snake_case      :							Optional[Any]     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Union[str, Any]     				=						original_images[0, -3:, -3:, -1]
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      LoraLoaderMixin.save_lora_weights(
                          save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  safe_serialization=lowercase_     ,  )
                      self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.safetensors'''					)					)					)
                      sd_pipe.load_lora_weights(lowercase_					)
  
               _snake_case      :							int     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Optional[int]     				=						lora_images[0, -3:, -3:, -1]
  
               # Outputs shouldn't match.
               self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						self.get_dummy_components()
               _snake_case      :							Union[str, Any]     				=						lora_components['''unet_lora_attn_procs''']
               _snake_case      :							List[Any]     				=						StableDiffusionPipeline(**lowercase_					)
               _snake_case      :							str     				=						sd_pipe.to(lowercase_					)
               sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
               _snake_case      :							Tuple     				=						self.get_dummy_inputs()
  
               _snake_case      :							Optional[int]     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							List[str]     				=						original_images[0, -3:, -3:, -1]
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      _snake_case      :							str     				=						sd_pipe.unet
                      unet.set_attn_processor(lowercase_					)
                      unet.save_attn_procs(lowercase_					)
                      self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                      sd_pipe.load_lora_weights(lowercase_					)
  
               _snake_case      :							str     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Tuple     				=						lora_images[0, -3:, -3:, -1]
  
               # Outputs shouldn't match.
               self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.get_dummy_components()
               _snake_case      :							List[Any]     				=						StableDiffusionPipeline(**lowercase_					)
  
               _snake_case      :							int     				=						self.get_dummy_tokens()
  
               # inference without lora
               _snake_case      :							Optional[int]     				=						pipe.text_encoder(**lowercase_					)[0]
               assert outputs_without_lora.shape == (1, 77, 32)
  
               # create lora_attn_procs with zeroed out up.weights
               _snake_case      :							List[str]     				=						create_text_encoder_lora_attn_procs(pipe.text_encoder					)
               set_lora_up_weights(lowercase_     ,  randn_weight=lowercase_					)
  
               # monkey patch
               pipe._modify_text_encoder(lowercase_					)
  
               # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
               del text_attn_procs
               gc.collect()
  
               # inference with lora
               _snake_case      :							List[str]     				=						pipe.text_encoder(**lowercase_					)[0]
               assert outputs_with_lora.shape == (1, 77, 32)
  
               assert torch.allclose(
                   lowercase_     ,  lowercase_					), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
  
               # create lora_attn_procs with randn up.weights
               _snake_case      :							Dict     				=						create_text_encoder_lora_attn_procs(pipe.text_encoder					)
               set_lora_up_weights(lowercase_     ,  randn_weight=lowercase_					)
  
               # monkey patch
               pipe._modify_text_encoder(lowercase_					)
  
               # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
               del text_attn_procs
               gc.collect()
  
               # inference with lora
               _snake_case      :							Dict     				=						pipe.text_encoder(**lowercase_					)[0]
               assert outputs_with_lora.shape == (1, 77, 32)
  
               assert not torch.allclose(
                   lowercase_     ,  lowercase_					), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.get_dummy_components()
               _snake_case      :							Optional[Any]     				=						StableDiffusionPipeline(**lowercase_					)
  
               _snake_case      :							int     				=						self.get_dummy_tokens()
  
               # inference without lora
               _snake_case      :							Optional[Any]     				=						pipe.text_encoder(**lowercase_					)[0]
               assert outputs_without_lora.shape == (1, 77, 32)
  
               # create lora_attn_procs with randn up.weights
               _snake_case      :							List[Any]     				=						create_text_encoder_lora_attn_procs(pipe.text_encoder					)
               set_lora_up_weights(lowercase_     ,  randn_weight=lowercase_					)
  
               # monkey patch
               pipe._modify_text_encoder(lowercase_					)
  
               # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
               del text_attn_procs
               gc.collect()
  
               # inference with lora
               _snake_case      :							List[Any]     				=						pipe.text_encoder(**lowercase_					)[0]
               assert outputs_with_lora.shape == (1, 77, 32)
  
               assert not torch.allclose(
                   lowercase_     ,  lowercase_					), "lora outputs should be different to without lora outputs"
  
               # remove monkey patch
               pipe._remove_text_encoder_monkey_patch()
  
               # inference with removed lora
               _snake_case      :							str     				=						pipe.text_encoder(**lowercase_					)[0]
               assert outputs_without_lora_removed.shape == (1, 77, 32)
  
               assert torch.allclose(
                   lowercase_     ,  lowercase_					), "remove lora monkey patch should restore the original outputs"
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.get_dummy_components()
               _snake_case      :							int     				=						StableDiffusionPipeline(**lowercase_					)
               _snake_case      :							List[Any]     				=						sd_pipe.to(lowercase_					)
               sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
               _snake_case      :							Union[str, Any]     				=						self.get_dummy_inputs()
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      LoraLoaderMixin.save_lora_weights(
                          save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
                      self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                      sd_pipe.load_lora_weights(lowercase_					)
  
               _snake_case      :							str     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Union[str, Any]     				=						lora_images[0, -3:, -3:, -1]
  
               _snake_case      :							Union[str, Any]     				=						sd_pipe(**lowercase_     ,  cross_attention_kwargs={'''scale''': 0.5}					).images
               _snake_case      :							Optional[int]     				=						lora_images_with_scale[0, -3:, -3:, -1]
  
               # Outputs shouldn't match.
               self.assertFalse(
                   torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      self.create_lora_weight_file(lowercase_					)
  
                      _snake_case      :							str     				=						self.get_dummy_components()
                      _snake_case      :							Optional[Any]     				=						StableDiffusionPipeline(**lowercase_					)
                      _snake_case      :							Any     				=						sd_pipe.to(lowercase_					)
                      sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
                      # check if vanilla attention processors are used
                      for _, module in sd_pipe.unet.named_modules():
                             if isinstance(lowercase_     ,  lowercase_					):
                                    self.assertIsInstance(module.processor     ,  (AttnProcessor, AttnProcessor2_0)					)
  
              # load LoRA weight file
                      sd_pipe.load_lora_weights(lowercase_					)
  
                      # check if lora attention processors are used
                      for _, module in sd_pipe.unet.named_modules():
                             if isinstance(lowercase_     ,  lowercase_					):
                                    _snake_case      :							str     				=						(
                                        LoRAAttnProcessor2_0 if hasattr(lowercase_     ,  '''scaled_dot_product_attention'''					) else LoRAAttnProcessor
                                    )
                                    self.assertIsInstance(module.processor     ,  lowercase_					)
  
  
  
        @unittest.skipIf(torch_device != '''cuda'''     ,  '''This test is supposed to run on GPU'''					)
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      self.create_lora_weight_file(lowercase_					)
  
                      _snake_case      :							Any     				=						self.get_dummy_components()
                      _snake_case      :							int     				=						StableDiffusionPipeline(**lowercase_					)
                      _snake_case      :							List[Any]     				=						sd_pipe.to(lowercase_					)
                      sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
                      # enable XFormers
                      sd_pipe.enable_xformers_memory_efficient_attention()
  
                      # check if xFormers attention processors are used
                      for _, module in sd_pipe.unet.named_modules():
                             if isinstance(lowercase_     ,  lowercase_					):
                                    self.assertIsInstance(module.processor     ,  lowercase_					)
  
              # load LoRA weight file
                      sd_pipe.load_lora_weights(lowercase_					)
  
                      # check if lora attention processors are used
                      for _, module in sd_pipe.unet.named_modules():
                             if isinstance(lowercase_     ,  lowercase_					):
                                    self.assertIsInstance(module.processor     ,  lowercase_					)
  
  
  
        @unittest.skipIf(torch_device != '''cuda'''     ,  '''This test is supposed to run on GPU'''					)
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.get_dummy_components()
               _snake_case      :							List[Any]     				=						StableDiffusionPipeline(**lowercase_					)
               _snake_case      :							Dict     				=						sd_pipe.to(lowercase_					)
               sd_pipe.set_progress_bar_config(disable=lowercase_					)
  
               _snake_case      :							Dict     				=						self.get_dummy_inputs()
  
               # enable XFormers
               sd_pipe.enable_xformers_memory_efficient_attention()
  
               _snake_case      :							List[Any]     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Tuple     				=						original_images[0, -3:, -3:, -1]
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                      LoraLoaderMixin.save_lora_weights(
                          save_directory=lowercase_     ,  unet_lora_layers=lora_components['''unet_lora_layers''']     ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']     ,  )
                      self.assertTrue(os.path.isfile(os.path.join(lowercase_     ,  '''pytorch_lora_weights.bin'''					)					)					)
                      sd_pipe.load_lora_weights(lowercase_					)
  
               _snake_case      :							Tuple     				=						sd_pipe(**lowercase_					).images
               _snake_case      :							Dict     				=						lora_images[0, -3:, -3:, -1]
  
               # Outputs shouldn't match.
               self.assertFalse(torch.allclose(torch.from_numpy(lowercase_					)     ,  torch.from_numpy(lowercase_					)					)					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						torch.Tensor:
         # The first operation in a checkpoint can't be in-place, but it's
         # nice to have in-place addition during inference. Thus...
         if not inplace:
                _snake_case      :							Any     				=						ma + ma
         else:
                ma += ma
  
         return ma
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						-1 * len(lowercase_  )
         _snake_case      :							Any     				=						list(range(len(tensor.shape[:zero_index]  )  )  )
         return tensor.permute(first_inds + [zero_index + i for i in inds]  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         return t.reshape(t.shape[:-no_dims] + (-1,)  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_ = 1E-4  )			->						torch.Tensor:
         _snake_case      :							Any     				=						mask.expand(*value.shape  )
         return torch.sum(mask * value      ,		dim=lowercase_  ) / (eps + torch.sum(lowercase_      ,		dim=lowercase_  ))
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_ = 2.31_25      ,		lowercase_ = 21.68_75      ,		lowercase_ = 64  )			->						torch.Tensor:
         _snake_case      :							int     				=						torch.linspace(lowercase_      ,		lowercase_      ,		no_bins - 1      ,		device=pts.device  )
         _snake_case      :							Optional[Any]     				=						torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2      ,		dim=-1  )  )
         return torch.bucketize(lowercase_      ,		lowercase_  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict:
         _snake_case      :							Dict     				=						dicts[0]
         _snake_case      :							str     				=						{}
         for k, v in first.items():
                _snake_case      :							List[Any]     				=						[d[k] for d in dicts]
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Any     				=						dict_multimap(lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							Dict     				=						fn(lowercase_  )
  
         return new_dict
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						v_bins.view(((1,) * len(x.shape  )) + (len(lowercase_  ),)  )
         _snake_case      :							Union[str, Any]     				=						x[..., None] - reshaped_bins
         _snake_case      :							Optional[Any]     				=						torch.argmin(torch.abs(lowercase_  )      ,		dim=-1  )
         return nn.functional.one_hot(lowercase_      ,		num_classes=len(lowercase_  )  ).float()
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = 0      ,		lowercase_ = 0  )			->						torch.Tensor:
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[]
         for i, s in enumerate(data.shape[:no_batch_dims]  ):
                _snake_case      :							Optional[Any]     				=						torch.arange(lowercase_  )
                _snake_case      :							Union[str, Any]     				=						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
                ranges.append(lowercase_  )
  
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[slice(lowercase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
         _snake_case      :							Union[str, Any]     				=						inds
         ranges.extend(lowercase_  )
         # Matt note: Editing this to get around the behaviour of using a list as an array index changing
         # in recent Numpy versions
         return data[tuple(lowercase_  )]
  
  
  lowerCAmelCase_   =   TypeVar("T")
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict[Any, Union[dict, list, tuple, Any]]:
         _snake_case      :							Dict[Any, Union[dict, list, tuple, Any]]     				=						{}
         for k, v in dic.items():
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Dict     				=						dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							List[Any]     				=						tree_map(lowercase_      ,		lowercase_      ,		lowercase_  )
  
         return new_dict
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						dict:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						list:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						tuple:
         ...
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         if isinstance(lowercase_      ,		lowercase_  ):
                return dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return [tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree]
         elif isinstance(lowercase_      ,		lowercase_  ):
                return tuple(tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return fn(lowercase_  )
         else:
                print(type(lowercase_  )  )
                raise ValueError('''Not supported'''  )
  lowerCAmelCase_   =   partial(tree_map, leaf_type=torch.Tensor)
  
  
  
  
  
  
  