





'''simple docstring'''

import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:    Union[str, Any] =       logging.get_logger(__name__)

lowercase__		:    str =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase__		:    Union[str, Any] =       {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

lowercase__		:    List[str] =       {
    'microsoft/biogpt': 10_24,
}







def       a__       (       lowercase      :  Union[str, Any]   )	->	List[Any]:





     """simple docstring"""

     _UpperCamelCase       					= set()
     _UpperCamelCase       					= word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)   )
          _UpperCamelCase       					= char
     return pairs




class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Dict		       =					VOCAB_FILES_NAMES
       _snake_case			:  List[str]		       =					PRETRAINED_VOCAB_FILES_MAP
       _snake_case			:  Tuple		       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case			:  Union[str, Any]		       =					['input_ids', 'attention_mask']





       def __init__(							self     :	List[Any] ,				lowerCAmelCase__     :	List[Any] ,				lowerCAmelCase__     :	Optional[Any] ,				lowerCAmelCase__     :	List[Any]="<unk>" ,				lowerCAmelCase__     :	Any="<s>" ,				lowerCAmelCase__     :	str="</s>" ,				lowerCAmelCase__     :	Dict="</s>" ,				lowerCAmelCase__     :	Optional[int]="<pad>" ,				**lowerCAmelCase__     :	Union[str, Any] ,				)					->  str:

            '''simple docstring'''




            super().__init__(
                bos_token=lowerCAmelCase__ ,				eos_token=lowerCAmelCase__ ,				sep_token=lowerCAmelCase__ ,				unk_token=lowerCAmelCase__ ,				pad_token=lowerCAmelCase__ ,				**lowerCAmelCase__ ,				)

            try:
                 import sacremoses
            except ImportError:
                 raise ImportError(
                     '''You need to install sacremoses to use BioGptTokenizer. '''
                     '''See https://pypi.org/project/sacremoses/ for installation.'''  )

            _UpperCamelCase       					= '''en'''
            _UpperCamelCase       					= sacremoses
            # cache of sm.MosesTokenizer instance
            _UpperCamelCase       					= {}
            _UpperCamelCase       					= {}
            with open(lowerCAmelCase__ ,				encoding='''utf-8'''  ) as vocab_handle:
                 _UpperCamelCase       					= json.load(lowerCAmelCase__  )
            _UpperCamelCase       					= {v: k for k, v in self.encoder.items()}
            with open(lowerCAmelCase__ ,				encoding='''utf-8'''  ) as merges_handle:
                 _UpperCamelCase       					= merges_handle.read().split('''\n'''  )[:-1]
            _UpperCamelCase       					= [tuple(merge.split()[:2]  ) for merge in merges]
            _UpperCamelCase       					= dict(zip(lowerCAmelCase__ ,				range(len(lowerCAmelCase__  )  )  )  )
            _UpperCamelCase       					= {}





       @property
       def snake_case__		(							self     :	str  )					->  Optional[int]:

            '''simple docstring'''




            return len(self.encoder  )





       def snake_case__		(							self     :	Optional[int]  )					->  List[Any]:

            '''simple docstring'''




            return dict(self.encoder ,				**self.added_tokens_encoder  )





       def snake_case__		(							self     :	Union[str, Any] ,				lowerCAmelCase__     :	Optional[int] ,				lowerCAmelCase__     :	Dict  )					->  int:

            '''simple docstring'''




            if lang not in self.cache_moses_tokenizer:
                 _UpperCamelCase       					= self.sm.MosesTokenizer(lang=lowerCAmelCase__  )
                 _UpperCamelCase       					= moses_tokenizer
            return self.cache_moses_tokenizer[lang].tokenize(
                lowerCAmelCase__ ,				aggressive_dash_splits=lowerCAmelCase__ ,				return_str=lowerCAmelCase__ ,				escape=lowerCAmelCase__  )





       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	int ,				lowerCAmelCase__     :	Any  )					->  List[str]:

            '''simple docstring'''




            if lang not in self.cache_moses_detokenizer:
                 _UpperCamelCase       					= self.sm.MosesDetokenizer(lang=lowerCAmelCase__  )
                 _UpperCamelCase       					= moses_detokenizer
            return self.cache_moses_detokenizer[lang].detokenize(lowerCAmelCase__  )





       def snake_case__		(							self     :	Optional[Any] ,				lowerCAmelCase__     :	Union[str, Any]  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= tuple(token[:-1]  ) + (token[-1] + '''</w>''',)
            if token in self.cache:
                 return self.cache[token]
            _UpperCamelCase       					= get_pairs(lowerCAmelCase__  )

            if not pairs:
                 return token + "</w>"

            while True:
                 _UpperCamelCase       					= min(lowerCAmelCase__ ,				key=lambda lowerCAmelCase__  : self.bpe_ranks.get(lowerCAmelCase__ ,				float('''inf'''  )  )  )
                 if bigram not in self.bpe_ranks:
                      break
                 _UpperCamelCase					,       _UpperCamelCase       					= bigram
                 _UpperCamelCase       					= []
                 _UpperCamelCase       					= 0
                 while i < len(lowerCAmelCase__  ):
                      try:
                           _UpperCamelCase       					= word.index(lowerCAmelCase__ ,				lowerCAmelCase__  )
                      except ValueError:
                           new_word.extend(word[i:]  )
                           break
                      else:
                           new_word.extend(word[i:j]  )
                           _UpperCamelCase       					= j

                      if word[i] == first and i < len(lowerCAmelCase__  ) - 1 and word[i + 1] == second:
                           new_word.append(first + second  )
                           i += 2
                      else:
                           new_word.append(word[i]  )
                           i += 1
                 _UpperCamelCase       					= tuple(lowerCAmelCase__  )
                 _UpperCamelCase       					= new_word
                 if len(lowerCAmelCase__  ) == 1:
                      break
                 else:
                      _UpperCamelCase       					= get_pairs(lowerCAmelCase__  )
            _UpperCamelCase       					= ''' '''.join(lowerCAmelCase__  )
            if word == "\n  </w>":
                 _UpperCamelCase       					= '''\n</w>'''
            _UpperCamelCase       					= word
            return word





       def snake_case__		(							self     :	Union[str, Any] ,				lowerCAmelCase__     :	Optional[int] ,				lowerCAmelCase__     :	Optional[Any]=False  )					->  Union[str, Any]:

            '''simple docstring'''




            if bypass_tokenizer:
                 _UpperCamelCase       					= text.split()
            else:
                 _UpperCamelCase       					= self.moses_tokenize(lowerCAmelCase__ ,				self.lang  )

            _UpperCamelCase       					= []
            for token in text:
                 if token:
                      split_tokens.extend(list(self.bpe(lowerCAmelCase__  ).split(''' '''  )  )  )

            return split_tokens





       def snake_case__		(							self     :	Union[str, Any] ,				lowerCAmelCase__     :	Any  )					->  Tuple:

            '''simple docstring'''




            return self.encoder.get(lowerCAmelCase__ ,				self.encoder.get(self.unk_token  )  )





       def snake_case__		(							self     :	List[Any] ,				lowerCAmelCase__     :	Dict  )					->  Dict:

            '''simple docstring'''




            return self.decoder.get(lowerCAmelCase__ ,				self.unk_token  )





       def snake_case__		(							self     :	Tuple ,				lowerCAmelCase__     :	Union[str, Any]  )					->  List[str]:

            '''simple docstring'''




            _UpperCamelCase       					= [t.replace(''' ''' ,				''''''  ).replace('''</w>''' ,				''' '''  ) for t in tokens]
            _UpperCamelCase       					= ''''''.join(lowerCAmelCase__  ).split()
            # detokenize
            _UpperCamelCase       					= self.moses_detokenize(lowerCAmelCase__ ,				self.lang  )
            return text





       def snake_case__		(							self     :	Any ,				lowerCAmelCase__     :	List[int] ,				lowerCAmelCase__     :	Optional[List[int]] = None  )					->  List[int]:

            '''simple docstring'''




            if token_ids_a is None:
                 return [self.sep_token_id] + token_ids_a
            _UpperCamelCase       					= [self.sep_token_id]
            return sep + token_ids_a + sep + token_ids_a





       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	List[int] ,				lowerCAmelCase__     :	Optional[List[int]] = None ,				lowerCAmelCase__     :	bool = False  )					->  List[int]:

            '''simple docstring'''




            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCAmelCase__ ,				token_ids_a=lowerCAmelCase__ ,				already_has_special_tokens=lowerCAmelCase__  )
            # no bos used in fairseq
            if token_ids_a is not None:
                 return [1] + ([0] * len(lowerCAmelCase__  )) + [1] + ([0] * len(lowerCAmelCase__  ))
            return [1] + ([0] * len(lowerCAmelCase__  ))





       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	List[int] ,				lowerCAmelCase__     :	Optional[List[int]] = None  )					->  List[int]:

            '''simple docstring'''




            _UpperCamelCase       					= [self.sep_token_id]

            # no bos used in fairseq
            if token_ids_a is None:
                 return len(token_ids_a + sep  ) * [0]
            return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	Optional[str] = None  )					->  Tuple[str]:

            '''simple docstring'''




            if not os.path.isdir(lowerCAmelCase__  ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
                 return
            _UpperCamelCase       					= os.path.join(
                lowerCAmelCase__ ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
            _UpperCamelCase       					= os.path.join(
                lowerCAmelCase__ ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

            with open(lowerCAmelCase__ ,				'''w''' ,				encoding='''utf-8'''  ) as f:
                 f.write(json.dumps(self.encoder ,				indent=2 ,				sort_keys=lowerCAmelCase__ ,				ensure_ascii=lowerCAmelCase__  ) + '''\n'''  )

            _UpperCamelCase       					= 0
            with open(lowerCAmelCase__ ,				'''w''' ,				encoding='''utf-8'''  ) as writer:
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,				key=lambda lowerCAmelCase__  : 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!'''  )
                           _UpperCamelCase       					= token_index
                      writer.write(''' '''.join(lowerCAmelCase__  ) + '''\n'''  )
                      index += 1

            return vocab_file, merge_file





       def __getstate__(							self     :	Optional[Any]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= self.__dict__.copy()
            _UpperCamelCase       					= None
            return state







       def __setstate__(							self     :	Optional[int] ,				lowerCAmelCase__     :	Optional[int]  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= d

            try:
                 import sacremoses
            except ImportError:
                 raise ImportError(
                     '''You need to install sacremoses to use XLMTokenizer. '''
                     '''See https://pypi.org/project/sacremoses/ for installation.'''  )

            _UpperCamelCase       					= sacremoses










'''simple docstring'''

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()

lowercase__		:    Union[str, Any] =       logging.get_logger(__name__)







def       a__       (       lowercase      :  Dict,      lowercase      :  bool = True   )	->	int:





     """simple docstring"""

     _UpperCamelCase       					= {}
     for name in model.attn_processors.keys():
          _UpperCamelCase       					= name.endswith('''attn2.processor'''   ) and not (
              name.startswith('''transformer_in'''   ) or '''temp_attentions''' in name.split('''.'''   )
          )
          _UpperCamelCase       					= model.config.cross_attention_dim if has_cross_attention else None
          if name.startswith('''mid_block'''   ):
               _UpperCamelCase       					= model.config.block_out_channels[-1]
          elif name.startswith('''up_blocks'''   ):
               _UpperCamelCase       					= int(name[len('''up_blocks.'''   )]   )
               _UpperCamelCase       					= list(reversed(model.config.block_out_channels   )   )[block_id]
          elif name.startswith('''down_blocks'''   ):
               _UpperCamelCase       					= int(name[len('''down_blocks.'''   )]   )
               _UpperCamelCase       					= 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
               _UpperCamelCase       					= 8 * model.config.attention_head_dim

          _UpperCamelCase       					= LoRAAttnProcessor(hidden_size=lowercase,      cross_attention_dim=lowercase   )
          _UpperCamelCase       					= 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        __lowerCAmelCase  (					__magic_name__		,		__magic_name__		,		unittest.TestCase				):






       """simple docstring"""
       _snake_case			:  int		       =					UNetaDConditionModel
       _snake_case			:  List[str]		       =					'sample'





       @property
       def snake_case__		(							self     :	Optional[int]  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= 4
            _UpperCamelCase       					= 4
            _UpperCamelCase       					= 4
            _UpperCamelCase       					= (32, 32)

            _UpperCamelCase       					= floats_tensor((batch_size, num_channels, num_frames) + sizes  ).to(lowerCAmelCase__  )
            _UpperCamelCase       					= torch.tensor([10]  ).to(lowerCAmelCase__  )
            _UpperCamelCase       					= floats_tensor((batch_size, 4, 32)  ).to(lowerCAmelCase__  )

            return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}





       @property
       def snake_case__		(							self     :	Tuple  )					->  Optional[int]:

            '''simple docstring'''




            return (4, 4, 32, 32)





       @property
       def snake_case__		(							self     :	Optional[Any]  )					->  Union[str, Any]:

            '''simple docstring'''




            return (4, 4, 32, 32)





       def snake_case__		(							self     :	Optional[Any]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= {
                '''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,
            }
            _UpperCamelCase       					= 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 snake_case__		(							self     :	Union[str, Any]  )					->  int:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )

            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 snake_case__		(							self     :	Tuple  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 32

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )

                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= output.sample

            self.assertIsNotNone(lowerCAmelCase__  )
            _UpperCamelCase       					= inputs_dict['''sample'''].shape
            self.assertEqual(output.shape ,				lowerCAmelCase__ ,				'''Input and output shapes do not match'''  )





       def snake_case__		(							self     :	str  )					->  Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 # Warmup pass when using mps (see #372)
                 if torch_device == "mps" and isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      model(**self.dummy_input  )

                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= first.sample

                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= second.sample

            _UpperCamelCase       					= first.cpu().numpy()
            _UpperCamelCase       					= second.cpu().numpy()
            _UpperCamelCase       					= out_a[~np.isnan(lowerCAmelCase__  )]
            _UpperCamelCase       					= out_a[~np.isnan(lowerCAmelCase__  )]
            _UpperCamelCase       					= np.amax(np.abs(out_a - out_a  )  )
            self.assertLessEqual(lowerCAmelCase__ ,				1e-5  )





       def snake_case__		(							self     :	Optional[Any]  )					->  str:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            model.set_attention_slice('''auto'''  )
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
            assert output is not None

            model.set_attention_slice('''max'''  )
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
            assert output is not None

            model.set_attention_slice(2  )
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
            assert output is not None





       def snake_case__		(							self     :	Optional[int]  )					->  Optional[int]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )

            # make sure we can set a list of attention processors
            model.set_attn_processor(lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            # test that attn processors can be set to itself
            model.set_attn_processor(model.attn_processors  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.0}  ).sample
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				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 snake_case__		(							self     :	str  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.bin'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= new_model(**lowerCAmelCase__ ,				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 snake_case__		(							self     :	str  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__ ,				safe_serialization=lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.safetensors'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= new_model(**lowerCAmelCase__ ,				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 snake_case__		(							self     :	Tuple  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__ ,				mock_weights=lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.bin'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__ ,				weight_name='''pytorch_lora_weights.bin'''  )





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__ ,				mock_weights=lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.bin'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 with self.assertRaises(lowerCAmelCase__  ) as e:
                      new_model.load_attn_procs(lowerCAmelCase__ ,				use_safetensors=lowerCAmelCase__  )
                 self.assertIn('''Error no file named pytorch_lora_weights.safetensors''' ,				str(e.exception  )  )





       def snake_case__		(							self     :	Tuple  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.0}  ).sample

            model.set_attn_processor(AttnProcessor()  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).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 snake_case__		(							self     :	List[str]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 4

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            # default
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

                 model.enable_xformers_memory_efficient_attention()
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

                 model.disable_xformers_memory_efficient_attention()
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            assert (sample - on_sample).abs().max() < 1e-4
            assert (sample - off_sample).abs().max() < 1e-4







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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= 32

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )[0]

            model.enable_forward_chunking()
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )[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.



