




import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowercase_		=							logging.getLogger(__name__)





@dataclass
class 		A__				:
	lowerCamelCase__		: str				    =field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
	lowerCamelCase__		: Optional[str]				    =field(
	    default=__SCREAMING_SNAKE_CASE		,     metadata={"help": "Pretrained config name or path if not the same as model_name"} )
	lowerCamelCase__		: Optional[str]				    =field(
	    default="NER"		,     metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
	lowerCamelCase__		: Optional[str]				    =field(
	    default=__SCREAMING_SNAKE_CASE		,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
	lowerCamelCase__		: bool				    =field(default=__SCREAMING_SNAKE_CASE		,     metadata={"help": "Set this flag to use fast tokenization."} )
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	lowerCamelCase__		: Optional[str]				    =field(
	    default=__SCREAMING_SNAKE_CASE		,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,     )





@dataclass
class 		A__				:
	lowerCamelCase__		: str				    =field(
	    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
	lowerCamelCase__		: Optional[str]				    =field(
	    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} )
	lowerCamelCase__		: int				    =field(
	    default=128		,     metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }		,     )
	lowerCamelCase__		: bool				    =field(
	    default=__SCREAMING_SNAKE_CASE		,     metadata={"help": "Overwrite the cached training and evaluation sets"} )







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






					"""simple docstring"""






					__magic_name__					:							str		   =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
					__magic_name__   , __magic_name__   , __magic_name__					:							Union[str, Any]		   =							parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir       )
					    and os.listdir(training_args.output_dir       )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ''' --overwrite_output_dir to overcome.'''       )

					__magic_name__					:							List[str]		   =							import_module('''tasks'''       )

					try:
										__magic_name__					:							List[str]		   =							getattr(UpperCAmelCase,	model_args.task_type       )
										__magic_name__					:							TokenClassificationTask		   =							token_classification_task_clazz()
					except AttributeError:
										raise ValueError(
										    F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
										    F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''       )

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',	datefmt='''%m/%d/%Y %H:%M:%S''',	level=logging.INFO,	)
					logger.info(
					    '''n_replicas: %s, distributed training: %s, 16-bits training: %s''',	training_args.n_replicas,	bool(training_args.n_replicas > 1       ),	training_args.fpaa,	)
					logger.info('''Training/evaluation parameters %s''',	UpperCAmelCase       )

					# Prepare Token Classification task
					__magic_name__					:							Any		   =							token_classification_task.get_labels(data_args.labels       )
					__magic_name__					:							Dict[int, str]		   =							dict(enumerate(UpperCAmelCase       )       )
					__magic_name__					:							Dict		   =							len(UpperCAmelCase       )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__magic_name__					:							List[str]		   =							AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path,	num_labels=UpperCAmelCase,	idalabel=UpperCAmelCase,	labelaid={label: i for i, label in enumerate(UpperCAmelCase       )},	cache_dir=model_args.cache_dir,	)
					__magic_name__					:							List[str]		   =							AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	use_fast=model_args.use_fast,	)

					with training_args.strategy.scope():
										__magic_name__					:							List[str]		   =							TFAutoModelForTokenClassification.from_pretrained(
										    model_args.model_name_or_path,	from_pt=bool('''.bin''' in model_args.model_name_or_path       ),	config=UpperCAmelCase,	cache_dir=model_args.cache_dir,	)

					# Get datasets
					__magic_name__					:							Tuple		   =							(
					    TFTokenClassificationDataset(
					        token_classification_task=UpperCAmelCase,	data_dir=data_args.data_dir,	tokenizer=UpperCAmelCase,	labels=UpperCAmelCase,	model_type=config.model_type,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	mode=Split.train,	)
					    if training_args.do_train
					    else None
					)
					__magic_name__					:							List[Any]		   =							(
					    TFTokenClassificationDataset(
					        token_classification_task=UpperCAmelCase,	data_dir=data_args.data_dir,	tokenizer=UpperCAmelCase,	labels=UpperCAmelCase,	model_type=config.model_type,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	mode=Split.dev,	)
					    if training_args.do_eval
					    else None
					)

					def align_predictions(UpperCAmelCase,	UpperCAmelCase       ) -> Tuple[List[int], List[int]]:
										__magic_name__					:							Optional[int]		   =							np.argmax(UpperCAmelCase,	axis=2       )
										__magic_name__   , __magic_name__					:							Any		   =							preds.shape
										__magic_name__					:							Optional[int]		   =							[[] for _ in range(UpperCAmelCase       )]
										__magic_name__					:							Union[str, Any]		   =							[[] for _ in range(UpperCAmelCase       )]

										for i in range(UpperCAmelCase       ):
															for j in range(UpperCAmelCase       ):
																				if label_ids[i, j] != -100:
																									out_label_list[i].append(label_map[label_ids[i][j]]       )
																									preds_list[i].append(label_map[preds[i][j]]       )

										return preds_list, out_label_list

					def compute_metrics(UpperCAmelCase       ) -> Dict:
										__magic_name__   , __magic_name__					:							Optional[int]		   =							align_predictions(p.predictions,	p.label_ids       )

										return {
										    "precision": precision_score(UpperCAmelCase,	UpperCAmelCase       ),
										    "recall": recall_score(UpperCAmelCase,	UpperCAmelCase       ),
										    "f1": fa_score(UpperCAmelCase,	UpperCAmelCase       ),
										}

					# Initialize our Trainer
					__magic_name__					:							Any		   =							TFTrainer(
					    model=UpperCAmelCase,	args=UpperCAmelCase,	train_dataset=train_dataset.get_dataset() if train_dataset else None,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,	compute_metrics=UpperCAmelCase,	)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir       )

					# Evaluation
					__magic_name__					:							List[str]		   =							{}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''       )

										__magic_name__					:							List[str]		   =							trainer.evaluate()
										__magic_name__					:							Tuple		   =							os.path.join(training_args.output_dir,	'''eval_results.txt'''       )

										with open(UpperCAmelCase,	'''w'''       ) as writer:
															logger.info('''***** Eval results *****'''       )

															for key, value in result.items():
																				logger.info('''  %s = %s''',	UpperCAmelCase,	UpperCAmelCase       )
																				writer.write('''%s = %s\n''' % (key, value)       )

															results.update(UpperCAmelCase       )

    # Predict
					if training_args.do_predict:
										__magic_name__					:							int		   =							TFTokenClassificationDataset(
										    token_classification_task=UpperCAmelCase,	data_dir=data_args.data_dir,	tokenizer=UpperCAmelCase,	labels=UpperCAmelCase,	model_type=config.model_type,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	mode=Split.test,	)

										__magic_name__   , __magic_name__   , __magic_name__					:							List[str]		   =							trainer.predict(test_dataset.get_dataset()       )
										__magic_name__   , __magic_name__					:							int		   =							align_predictions(UpperCAmelCase,	UpperCAmelCase       )
										__magic_name__					:							List[str]		   =							classification_report(UpperCAmelCase,	UpperCAmelCase       )

										logger.info('''\n%s''',	UpperCAmelCase       )

										__magic_name__					:							Optional[int]		   =							os.path.join(training_args.output_dir,	'''test_results.txt'''       )

										with open(UpperCAmelCase,	'''w'''       ) as writer:
															writer.write('''%s\n''' % report       )

										# Save predictions
										__magic_name__					:							List[str]		   =							os.path.join(training_args.output_dir,	'''test_predictions.txt'''       )

										with open(UpperCAmelCase,	'''w'''       ) as writer:
															with open(os.path.join(data_args.data_dir,	'''test.txt'''       ),	'''r'''       ) as f:
																				__magic_name__					:							str		   =							0

																				for line in f:
																									if line.startswith('''-DOCSTART-'''       ) or line == "" or line == "\n":
																														writer.write(UpperCAmelCase       )

																														if not preds_list[example_id]:
																																			example_id += 1
																									elif preds_list[example_id]:
																														__magic_name__					:							Dict		   =							line.split()[0] + ''' ''' + preds_list[example_id].pop(0       ) + '''\n'''

																														writer.write(UpperCAmelCase       )
																									else:
																														logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''',	line.split()[0]       )

					return results


if __name__ == "__main__":
				main()










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()

_UpperCAmelCase				       = logging.get_logger(__name__)




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_ = True     )   ->						Any:
       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=UpperCamelCase_    ,						cross_attention_dim=UpperCamelCase_     )
              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 							_UpperCamelCase    (		lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase   ):
       _UpperCamelCase :    Optional[Any]   			=      UNetaDConditionModel
       _UpperCamelCase :    List[Any]   			=      '''sample'''

       @property
       def lowercase       (						self:       Dict						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     (32, 32)

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

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

       @property
       def lowercase       (						self:       Tuple						)				->       str:




              """simple docstring"""


              return (4, 4, 32, 32)

       @property
       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              return (4, 4, 32, 32)

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




              """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 lowercase       (						self:       str						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)

              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 lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)

                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     output.sample

              self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     inputs_dict["sample"].shape
              self.assertEqual(output.shape    ,      _SCREAMING_SNAKE_CASE    ,      "Input and output shapes do not match"						)

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

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

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     first.sample

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     second.sample

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

       def lowercase       (						self:       int						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              model.set_attention_slice("auto"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

              model.set_attention_slice("max"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

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

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)

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

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

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      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 lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

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

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      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 lowercase       (						self:       List[str]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

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

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      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 lowercase       (						self:       int						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

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

       def lowercase       (						self:       Any						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

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

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample

              model.set_attn_processor(AttnProcessor()						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).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 lowercase       (						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     4

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              # default
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.enable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.disable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

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



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




              """simple docstring"""


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

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              model.enable_forward_chunking()
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[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.

