
'''simple docstring'''







import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCAmelCase_      =   logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def   lowerCAmelCase(  a__    :  Any      ):







       '''simple docstring'''






       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None







def   lowerCAmelCase(  a__    :  Tuple      , a__    :  int      , a__    :  List[str]      ):







       '''simple docstring'''






       return max(metric_fn(a__      , a__      ) for gt in ground_truths      )







def   lowerCAmelCase(  a__    :  Dict      , a__    :  Optional[int]      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      [line.strip() for line in open(a__      , "r"      ).readlines()]
       lowerCamelCase__				   =      []

       if args.gold_data_mode == "qa":
              lowerCamelCase__				   =      pd.read_csv(a__      , sep="\t"      , header=a__      )
              for answer_list in data[1]:
                     lowerCamelCase__				   =      ast.literal_eval(a__      )
                     answers.append(a__      )
       else:
              lowerCamelCase__				   =      [line.strip() for line in open(a__      , "r"      ).readlines()]
              lowerCamelCase__				   =      [[reference] for reference in references]

       lowerCamelCase__				   =      lowerCamelCase__				   =      lowerCamelCase__				   =      0
       for prediction, ground_truths in zip(a__      , a__      ):
              total += 1
              em += metric_max_over_ground_truths(a__      , a__      , a__      )
              fa += metric_max_over_ground_truths(a__      , a__      , a__      )

       lowerCamelCase__				   =      1_0_0.0 * em / total
       lowerCamelCase__				   =      1_0_0.0 * fa / total

       logger.info(f"""F1: {fa:.2f}"""      )
       logger.info(f"""EM: {em:.2f}"""      )







def   lowerCAmelCase(  a__    :  int      , a__    :  List[Any]      , a__    :  Union[str, Any]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      args.k
       lowerCamelCase__				   =      [line.strip() for line in open(a__      , "r"      ).readlines()]
       lowerCamelCase__				   =      [line.strip() for line in open(a__      , "r"      ).readlines()]

       lowerCamelCase__				   =      lowerCamelCase__				   =      0
       for hypo, reference in zip(a__      , a__      ):
              lowerCamelCase__				   =      set(hypo.split("\t"      )[:k]      )
              lowerCamelCase__				   =      set(reference.split("\t"      )      )
              total += 1
              em += len(hypo_provenance & ref_provenance      ) / k

       lowerCamelCase__				   =      1_0_0.0 * em / total
       logger.info(f"""Precision@{k}: {em: .2f}"""      )







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  List[str]      , a__    :  str      ):







       '''simple docstring'''






       def strip_title(a__    :  Union[str, Any]      ):
              if title.startswith("\""      ):
                     lowerCamelCase__				   =      title[1:]
              if title.endswith("\""      ):
                     lowerCamelCase__				   =      title[:-1]
              return title

       lowerCamelCase__				   =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           a__      , return_tensors="pt"      , padding=a__      , truncation=a__      , )["input_ids"].to(args.device      )

       lowerCamelCase__				   =      rag_model.rag.question_encoder(a__      )
       lowerCamelCase__				   =      question_enc_outputs[0]

       lowerCamelCase__				   =      rag_model.retriever(
           a__      , question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy()      , prefix=rag_model.rag.generator.config.prefix      , n_docs=rag_model.config.n_docs      , return_tensors="pt"      , )
       lowerCamelCase__				   =      rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
       lowerCamelCase__				   =      []
       for docs in all_docs:
              lowerCamelCase__				   =      [strip_title(a__      ) for title in docs["title"]]
              provenance_strings.append("\t".join(a__      )      )
       return provenance_strings







def   lowerCAmelCase(  a__    :  str      , a__    :  Optional[Any]      , a__    :  List[str]      ):







       '''simple docstring'''






       with torch.no_grad():
              lowerCamelCase__				   =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  a__      , return_tensors="pt"      , padding=a__      , truncation=a__      )

              lowerCamelCase__				   =      inputs_dict.input_ids.to(args.device      )
              lowerCamelCase__				   =      inputs_dict.attention_mask.to(args.device      )
              lowerCamelCase__				   =      rag_model.generate(  # rag_model overwrites generate
                  a__      , attention_mask=a__      , num_beams=args.num_beams      , min_length=args.min_length      , max_length=args.max_length      , early_stopping=a__      , num_return_sequences=1      , bad_words_ids=[[0, 0]]      , )
              lowerCamelCase__				   =      rag_model.retriever.generator_tokenizer.batch_decode(a__      , skip_special_tokens=a__      )

              if args.print_predictions:
                     for q, a in zip(a__      , a__      ):
                            logger.info("Q: {} - A: {}".format(a__      , a__      )      )

              return answers







def   lowerCAmelCase(  ):







       '''simple docstring'''






       lowerCamelCase__				   =      argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"      , choices=["rag_sequence", "rag_token", "bart"]      , type=a__      , help=(
               "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
               " model_name_or_path"
           )      , )
       parser.add_argument(
           "--index_name"      , default=a__      , choices=["exact", "compressed", "legacy"]      , type=a__      , help="RAG model retriever type"      , )
       parser.add_argument(
           "--index_path"      , default=a__      , type=a__      , help="Path to the retrieval index"      , )
       parser.add_argument("--n_docs"      , default=5      , type=a__      , help="Number of retrieved docs"      )
       parser.add_argument(
           "--model_name_or_path"      , default=a__      , type=a__      , required=a__      , help="Path to pretrained checkpoints or model identifier from huggingface.co/models"      , )
       parser.add_argument(
           "--eval_mode"      , choices=["e2e", "retrieval"]      , default="e2e"      , type=a__      , help=(
               "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
               " precision@k."
           )      , )
       parser.add_argument("--k"      , default=1      , type=a__      , help="k for the precision@k calculation"      )
       parser.add_argument(
           "--evaluation_set"      , default=a__      , type=a__      , required=a__      , help="Path to a file containing evaluation samples"      , )
       parser.add_argument(
           "--gold_data_path"      , default=a__      , type=a__      , required=a__      , help="Path to a tab-separated file with gold samples"      , )
       parser.add_argument(
           "--gold_data_mode"      , default="qa"      , type=a__      , choices=["qa", "ans"]      , help=(
               "Format of the gold data file"
               "qa - a single line in the following format: question [tab] answer_list"
               "ans - a single line of the gold file contains the expected answer string"
           )      , )
       parser.add_argument(
           "--predictions_path"      , type=a__      , default="predictions.txt"      , help="Name of the predictions file, to be stored in the checkpoints directory"      , )
       parser.add_argument(
           "--eval_all_checkpoints"      , action="store_true"      , help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"      , )
       parser.add_argument(
           "--eval_batch_size"      , default=8      , type=a__      , help="Batch size per GPU/CPU for evaluation."      , )
       parser.add_argument(
           "--recalculate"      , help="Recalculate predictions even if the prediction file exists"      , action="store_true"      , )
       parser.add_argument(
           "--num_beams"      , default=4      , type=a__      , help="Number of beams to be used when generating answers"      , )
       parser.add_argument("--min_length"      , default=1      , type=a__      , help="Min length of the generated answers"      )
       parser.add_argument("--max_length"      , default=50      , type=a__      , help="Max length of the generated answers"      )

       parser.add_argument(
           "--print_predictions"      , action="store_true"      , help="If True, prints predictions while evaluating."      , )
       parser.add_argument(
           "--print_docs"      , action="store_true"      , help="If True, prints docs retried while generating."      , )
       lowerCamelCase__				   =      parser.parse_args()
       lowerCamelCase__				   =      torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
       return args







def   lowerCAmelCase(  a__    :  Any      ):







       '''simple docstring'''






       lowerCamelCase__				   =      {}
       if args.model_type is None:
              lowerCamelCase__				   =      infer_model_type(args.model_name_or_path      )
              assert args.model_type is not None
       if args.model_type.startswith("rag"      ):
              lowerCamelCase__				   =      RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              lowerCamelCase__				   =      args.n_docs
              if args.index_name is not None:
                     lowerCamelCase__				   =      args.index_name
              if args.index_path is not None:
                     lowerCamelCase__				   =      args.index_path
       else:
              lowerCamelCase__				   =      BartForConditionalGeneration

       lowerCamelCase__				   =      (
           [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info("Evaluate the following checkpoints: %s"      , a__      )

       lowerCamelCase__				   =      get_scores if args.eval_mode == "e2e" else get_precision_at_k
       lowerCamelCase__				   =      evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path      ) and (not args.recalculate):
                     logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path      )      )
                     score_fn(a__      , args.predictions_path      , args.gold_data_path      )
                     continue

              logger.info("***** Running evaluation for {} *****".format(a__      )      )
              logger.info("  Batch size = %d"      , args.eval_batch_size      )
              logger.info("  Predictions will be stored under {}".format(args.predictions_path      )      )

              if args.model_type.startswith("rag"      ):
                     lowerCamelCase__				   =      RagRetriever.from_pretrained(a__      , **a__      )
                     lowerCamelCase__				   =      model_class.from_pretrained(a__      , retriever=a__      , **a__      )
                     model.retriever.init_retrieval()
              else:
                     lowerCamelCase__				   =      model_class.from_pretrained(a__      , **a__      )
              model.to(args.device      )

              with open(args.evaluation_set      , "r"      ) as eval_file, open(args.predictions_path      , "w"      ) as preds_file:
                     lowerCamelCase__				   =      []
                     for line in tqdm(a__      ):
                            questions.append(line.strip()      )
                            if len(a__      ) == args.eval_batch_size:
                                   lowerCamelCase__				   =      evaluate_batch_fn(a__      , a__      , a__      )
                                   preds_file.write("\n".join(a__      ) + "\n"      )
                                   preds_file.flush()
                                   lowerCamelCase__				   =      []
                     if len(a__      ) > 0:
                            lowerCamelCase__				   =      evaluate_batch_fn(a__      , a__      , a__      )
                            preds_file.write("\n".join(a__      )      )
                            preds_file.flush()

                     score_fn(a__      , args.predictions_path      , args.gold_data_path      )


if __name__ == "__main__":
    lowerCAmelCase_      =   get_args()
    main(args)



'''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()

lowerCAmelCase_      =   logging.get_logger(__name__)







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  bool = True      ):







       '''simple docstring'''






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

              lowerCamelCase__				   =      LoRAAttnProcessor(hidden_size=a__      , cross_attention_dim=a__      )
              lowerCamelCase__				   =      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_       (						A__		,	A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[int]								=UNetaDConditionModel
   __lowerCAmelCase	:							int								='''sample'''







   @property
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      4
          lowerCamelCase__				   =      4
          lowerCamelCase__				   =      4
          lowerCamelCase__				   =      (32, 32)

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

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







   @property
   def     __UpperCAmelCase      ( self):
          return (4, 4, 32, 32)







   @property
   def     __UpperCAmelCase      ( self):
          return (4, 4, 32, 32)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {
              "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,
          }
          lowerCamelCase__				   =      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     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)

          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     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      32

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)

                 if isinstance(UpperCamelCase			,   UpperCamelCase):
                        lowerCamelCase__				   =      output.sample

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

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

                 lowerCamelCase__				   =      model(**UpperCamelCase)
                 if isinstance(UpperCamelCase			,   UpperCamelCase):
                        lowerCamelCase__				   =      first.sample

                 lowerCamelCase__				   =      model(**UpperCamelCase)
                 if isinstance(UpperCamelCase			,   UpperCamelCase):
                        lowerCamelCase__				   =      second.sample

          lowerCamelCase__				   =      first.cpu().numpy()
          lowerCamelCase__				   =      second.cpu().numpy()
          lowerCamelCase__				   =      out_a[~np.isnan(UpperCamelCase)]
          lowerCamelCase__				   =      out_a[~np.isnan(UpperCamelCase)]
          lowerCamelCase__				   =      np.amax(np.abs(out_a - out_a))
          self.assertLessEqual(UpperCamelCase			,   1E-5)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

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

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

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)

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

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

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.0}).sample
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   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     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

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

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

          with torch.no_grad():
                 lowerCamelCase__				   =      new_model(**UpperCamelCase			,   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     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

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

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

          with torch.no_grad():
                 lowerCamelCase__				   =      new_model(**UpperCamelCase			,   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     __UpperCAmelCase      ( self):
          # enable deterministic behavior for gradient checkpointing
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

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

          model.set_attn_processor(AttnProcessor())

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).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     __UpperCAmelCase      ( self):
          # enable deterministic behavior for gradient checkpointing
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      4

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

          # default
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

                 model.enable_xformers_memory_efficient_attention()
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

                 model.disable_xformers_memory_efficient_attention()
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__				   =      32

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)[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.

