code: 
  import json
  import os
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  if TYPE_CHECKING:
  				from transformers.pipelines.conversational import Conversation
  
  snake_case				:	List[str]							   =						logging.get_logger(__name__)
  
  snake_case				:	Dict							   =						{"vocab_file": "vocab.json", "merges_file": "merges.txt"}
  
  snake_case				:	Union[str, Any]							   =						{
      "vocab_file": {
          "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
          "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
          "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
          "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
          "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
          "microsoft/deberta-xlarge-mnli": (
              "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
          ),
      },
      "merges_file": {
          "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
          "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
          "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
          "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
          "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
          "microsoft/deberta-xlarge-mnli": (
              "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
          ),
      },
  }
  
  snake_case				:	Dict							   =						{
      "microsoft/deberta-base": 512,
      "microsoft/deberta-large": 512,
      "microsoft/deberta-xlarge": 512,
      "microsoft/deberta-base-mnli": 512,
      "microsoft/deberta-large-mnli": 512,
      "microsoft/deberta-xlarge-mnli": 512,
  }
  
  snake_case				:	List[str]							   =						{
      "microsoft/deberta-base": {"do_lower_case": False},
      "microsoft/deberta-large": {"do_lower_case": False},
  }
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Any			     =  (
  	    list(range(ord("!"     )   ,     ord("~"     ) + 1     )     ) + list(range(ord("¡"     )   ,     ord("¬"     ) + 1     )     ) + list(range(ord("®"     )   ,     ord("ÿ"     ) + 1     )     )
  	)
  	__magic_name__	:	Optional[Any]			     =  bs[:]
  	__magic_name__	:	List[Any]			     =  0
  	for b in range(2**8     ):
  		if b not in bs:
  			bs.append(_snake_case     )
  			cs.append(2**8 + n     )
  			n += 1
  	__magic_name__	:	Optional[Any]			     =  [chr(_snake_case     ) for n in cs]
  	return dict(zip(_snake_case   ,     _snake_case     )     )
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  set()
  	__magic_name__	:	List[Any]			     =  word[0]
  	for char in word[1:]:
  		pairs.add((prev_char, char)     )
  		__magic_name__	:	List[str]			     =  char
  	return pairs
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask', 'token_type_ids']
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a="replace"		,     _a="[CLS]"		,     _a="[SEP]"		,     _a="[SEP]"		,     _a="[CLS]"		,     _a="[UNK]"		,     _a="[PAD]"		,     _a="[MASK]"		,     _a=False		,     _a=False		,     **_a		,     ):
  				__magic_name__	:	int			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else bos_token
  				__magic_name__	:	str			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else eos_token
  				__magic_name__	:	Any			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else sep_token
  				__magic_name__	:	Optional[int]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else cls_token
  				__magic_name__	:	int			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else unk_token
  				__magic_name__	:	Dict			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else pad_token
  
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	Union[str, Any]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    errors=_a		,     bos_token=_a		,     eos_token=_a		,     unk_token=_a		,     sep_token=_a		,     cls_token=_a		,     pad_token=_a		,     mask_token=_a		,     add_prefix_space=_a		,     add_bos_token=_a		,     **_a		,     )
  				__magic_name__	:	Union[str, Any]			     =  add_bos_token
  
  				with open(_a		,     encoding="utf-8"	) as vocab_handle:
  					__magic_name__	:	int			     =  json.load(_a	)
  				__magic_name__	:	str			     =  {v: k for k, v in self.encoder.items()}
  				__magic_name__	:	Tuple			     =  errors  # how to handle errors in decoding
  				__magic_name__	:	str			     =  bytes_to_unicode()
  				__magic_name__	:	Union[str, Any]			     =  {v: k for k, v in self.byte_encoder.items()}
  				with open(_a		,     encoding="utf-8"	) as merges_handle:
  					__magic_name__	:	int			     =  merges_handle.read().split("\n"	)[1:-1]
  				__magic_name__	:	Tuple			     =  [tuple(merge.split()	) for merge in bpe_merges]
  				__magic_name__	:	str			     =  dict(zip(_a		,     range(len(_a	)	)	)	)
  				__magic_name__	:	Union[str, Any]			     =  {}
  				__magic_name__	:	Optional[Any]			     =  add_prefix_space
  
  				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  				__magic_name__	:	Optional[int]			     =  re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)
  
  
  
  
  
  
  
  			@property
  			# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return len(self.encoder	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return dict(self.encoder		,     **self.added_tokens_encoder	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				if token in self.cache:
  					return self.cache[token]
  				__magic_name__	:	Any			     =  tuple(_a	)
  				__magic_name__	:	str			     =  get_pairs(_a	)
  
  				if not pairs:
  					return token
  
  				while True:
  					__magic_name__	:	Optional[int]			     =  min(_a		,     key=lambda _a	: self.bpe_ranks.get(_a		,     float("inf"	)	)	)
  					if bigram not in self.bpe_ranks:
  						break
  					__magic_name__     ,					__magic_name__	:	int			     =  bigram
  					__magic_name__	:	int			     =  []
  					__magic_name__	:	Union[str, Any]			     =  0
  					while i < len(_a	):
  						try:
  							__magic_name__	:	Optional[Any]			     =  word.index(_a		,     _a	)
  						except ValueError:
  							new_word.extend(word[i:]	)
  							break
  						else:
  							new_word.extend(word[i:j]	)
  							__magic_name__	:	List[Any]			     =  j
  
  						if word[i] == first and i < len(_a	) - 1 and word[i + 1] == second:
  							new_word.append(first + second	)
  							i += 2
  						else:
  							new_word.append(word[i]	)
  							i += 1
  					__magic_name__	:	Any			     =  tuple(_a	)
  					__magic_name__	:	str			     =  new_word
  					if len(_a	) == 1:
  						break
  					else:
  						__magic_name__	:	Union[str, Any]			     =  get_pairs(_a	)
  				__magic_name__	:	str			     =  " ".join(_a	)
  				__magic_name__	:	List[str]			     =  word
  				return word
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	List[Any]			     =  [self.cls_token_id]
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None		,     _a = False	):
  				if already_has_special_tokens:
  					return super().get_special_tokens_mask(
  					    token_ids_a=_a		,     token_ids_a=_a		,     already_has_special_tokens=_a	)
  
  				if token_ids_a is None:
  					return [1] + ([0] * len(_a	)) + [1]
  				return [1] + ([0] * len(_a	)) + [1] + ([0] * len(_a	)) + [1]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	Any			     =  [self.sep_token_id]
  				__magic_name__	:	str			     =  [self.cls_token_id]
  
  				if token_ids_a is None:
  					return len(cls + token_ids_a + sep	) * [0]
  				return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  []
  				for token in re.findall(self.pat		,     _a	):
  					__magic_name__	:	Optional[int]			     =  "".join(
  					    self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_a	).split(" "	)	)
  				return bpe_tokens
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				return self.encoder.get(_a		,     self.encoder.get(self.unk_token	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				return self.decoder.get(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	List[Any]			     =  "".join(_a	)
  				__magic_name__	:	List[Any]			     =  bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"		,     errors=self.errors	)
  				return text
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  				__magic_name__	:	List[str]			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)
  
  				with open(_a		,     "w"		,     encoding="utf-8"	) as f:
  					f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=_a		,     ensure_ascii=_a	) + "\n"	)
  
  				__magic_name__	:	Optional[Any]			     =  0
  				with open(_a		,     "w"		,     encoding="utf-8"	) as writer:
  					writer.write("#version: 0.2\n"	)
  					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda _a	: kv[1]	):
  						if index != token_index:
  							logger.warning(
  							    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
  							    " Please check that the tokenizer is not corrupted!"	)
  							__magic_name__	:	Union[str, Any]			     =  token_index
  						writer.write(" ".join(_a	) + "\n"	)
  						index += 1
  
  				return vocab_file, merge_file
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=False		,     **_a	):
  				__magic_name__	:	str			     =  kwargs.pop("add_prefix_space"		,     self.add_prefix_space	)
  				if (is_split_into_words or add_prefix_space) and (len(_a	) > 0 and not text[0].isspace()):
  					__magic_name__	:	str			     =  " " + text
  				return (text, kwargs)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	List[str]			     =  []
  				for is_user, text in conversation.iter_texts():
  					input_ids.extend(self.encode(_a		,     add_special_tokens=_a	) + [self.eos_token_id]	)
  				if len(_a	) > self.model_max_length:
  					__magic_name__	:	Union[str, Any]			     =  input_ids[-self.model_max_length :]
  				return input_ids
  
style_context: 
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      torch_device,
  )
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  enable_full_determinism()
  
  snake_case				:	int							   =						logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						bool = True     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  {}
  	for name in model.attn_processors.keys():
  		__magic_name__	:	List[Any]			     =  name.endswith("attn2.processor"     ) and not (
  		    name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
  		)
  		__magic_name__	:	List[str]			     =  model.config.cross_attention_dim if has_cross_attention else None
  		if name.startswith("mid_block"     ):
  			__magic_name__	:	Tuple			     =  model.config.block_out_channels[-1]
  		elif name.startswith("up_blocks"     ):
  			__magic_name__	:	Tuple			     =  int(name[len("up_blocks."     )]     )
  			__magic_name__	:	Optional[Any]			     =  list(reversed(model.config.block_out_channels     )     )[block_id]
  		elif name.startswith("down_blocks"     ):
  			__magic_name__	:	Optional[int]			     =  int(name[len("down_blocks."     )]     )
  			__magic_name__	:	str			     =  model.config.block_out_channels[block_id]
  		elif name.startswith("transformer_in"     ):
  			# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
  			__magic_name__	:	List[Any]			     =  8 * model.config.attention_head_dim
  
  		__magic_name__	:	Union[str, Any]			     =  LoRAAttnProcessor(hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
  		__magic_name__	:	str			     =  lora_attn_procs[name].to(model.device     )
  
  		if mock_weights:
  			# add 1 to weights to mock trained weights
  			with torch.no_grad():
  				lora_attn_procs[name].to_q_lora.up.weight += 1
  				lora_attn_procs[name].to_k_lora.up.weight += 1
  				lora_attn_procs[name].to_v_lora.up.weight += 1
  				lora_attn_procs[name].to_out_lora.up.weight += 1
  
  	return lora_attn_procs
  
  
  
  
  
  @skip_mps
  class    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					UNetaDConditionModel
  			UpperCamelCase__					   =					'sample'
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  4
  				__magic_name__	:	str			     =  4
  				__magic_name__	:	List[str]			     =  4
  				__magic_name__	:	Optional[int]			     =  (32, 32)
  
  				__magic_name__	:	Optional[Any]			     =  floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(_a	)
  				__magic_name__	:	Union[str, Any]			     =  torch.tensor([10]	).to(_a	)
  				__magic_name__	:	List[str]			     =  floats_tensor((batch_size, 4, 32)	).to(_a	)
  
  				return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  {
  				    "block_out_channels": (32, 64),
  				    "down_block_types": (
  				        "CrossAttnDownBlock3D",
  				        "DownBlock3D",
  				    ),
  				    "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
  				    "cross_attention_dim": 32,
  				    "attention_head_dim": 8,
  				    "out_channels": 4,
  				    "in_channels": 4,
  				    "layers_per_block": 1,
  				    "sample_size": 32,
  				}
  				__magic_name__	:	List[str]			     =  self.dummy_input
  				return init_dict, inputs_dict
  
  
  
  
  
  
  
  			@unittest.skipIf(
  			    torch_device != "cuda" or not is_xformers_available()		,     reason="XFormers attention is only available with CUDA and `xformers` installed"		,     )
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Any			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  
  				model.enable_xformers_memory_efficient_attention()
  
  				assert (
  				    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
  				    == "XFormersAttnProcessor"
  				), "xformers is not enabled"
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  32
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	)
  
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	Any			     =  output.sample
  
  				self.assertIsNotNone(_a	)
  				__magic_name__	:	Union[str, Any]			     =  inputs_dict["sample"].shape
  				self.assertEqual(output.shape		,     _a		,     "Input and output shapes do not match"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					# Warmup pass when using mps (see #372)
  					if torch_device == "mps" and isinstance(_a		,     _a	):
  						model(**self.dummy_input	)
  
  					__magic_name__	:	str			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  first.sample
  
  					__magic_name__	:	Tuple			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  second.sample
  
  				__magic_name__	:	Optional[int]			     =  first.cpu().numpy()
  				__magic_name__	:	Any			     =  second.cpu().numpy()
  				__magic_name__	:	List[Any]			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Any			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Tuple			     =  np.amax(np.abs(out_a - out_a	)	)
  				self.assertLessEqual(_a		,     1e-5	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Union[str, Any]			     =  8
  
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				model.set_attention_slice("auto"	)
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice("max"	)
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice(2	)
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	)
  				assert output is not None
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[Any]			     =  8
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  
  				# make sure we can set a list of attention processors
  				model.set_attn_processor(_a	)
  				model.to(_a	)
  
  				# test that attn processors can be set to itself
  				model.set_attn_processor(model.attn_processors	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  					__magic_name__	:	Union[str, Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				assert (samplea - samplea).abs().max() < 3e-3
  				assert (samplea - samplea).abs().max() < 3e-3
  
  				# sample 2 and sample 3 should be different
  				assert (samplea - samplea).abs().max() > 3e-3
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  new_model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				assert (sample - new_sample).abs().max() < 1e-3
  
  				# LoRA and no LoRA should NOT be the same
  				assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Optional[int]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a		,     safe_serialization=_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.safetensors"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  new_model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				assert (sample - new_sample).abs().max() < 3e-3
  
  				# LoRA and no LoRA should NOT be the same
  				assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	Any			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a		,     weight_name="pytorch_lora_weights.bin"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	str			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					with self.assertRaises(_a	) as e:
  						new_model.load_attn_procs(_a		,     use_safetensors=_a	)
  					self.assertIn("Error no file named pytorch_lora_weights.safetensors"		,     str(e.exception	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  
  				model.set_attn_processor(AttnProcessor()	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample
  
  				assert (sample - new_sample).abs().max() < 1e-4
  				assert (sample - old_sample).abs().max() < 3e-3
  
  
  
  
  
  
  
  			@unittest.skipIf(
  			    torch_device != "cuda" or not is_xformers_available()		,     reason="XFormers attention is only available with CUDA and `xformers` installed"		,     )
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  4
  
  				torch.manual_seed(0	)
  				__magic_name__	:	str			     =  self.model_class(**_a	)
  				model.to(_a	)
  				__magic_name__	:	Any			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				# default
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	).sample
  
  					model.enable_xformers_memory_efficient_attention()
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  					model.disable_xformers_memory_efficient_attention()
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample
  
  				assert (sample - on_sample).abs().max() < 1e-4
  				assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	str			     =  32
  
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	)[0]
  
  				model.enable_forward_chunking()
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	)[0]
  
  				self.assertEqual(output.shape		,     output_a.shape		,     "Shape doesn't match"	)
  				assert np.abs(output.cpu() - output_a.cpu()	).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  