


'''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


a__    :   List[str]								=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def _UpperCamelCase			(	__A       )				->						int:


      '''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 _UpperCamelCase			(	__A ,     __A ,     __A       )				->						Any:


      '''simple docstring'''



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







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							[line.strip() for line in open(__lowerCamelCase ,     "r"       ).readlines()]
      UpperCamelCase__      =							[]

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

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

      UpperCamelCase__      =							100.0 * em / total
      UpperCamelCase__      =							100.0 * fa / total

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







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						Optional[Any]:


      '''simple docstring'''



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

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

      UpperCamelCase__      =							100.0 * em / total
      logger.info(F'''Precision@{k}: {em: .2f}'''       )







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      def strip_title(__A       ):
            if title.startswith("\""       ):
                  UpperCamelCase__      =							title[1:]
            if title.endswith("\""       ):
                  UpperCamelCase__      =							title[:-1]
            return title

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

      UpperCamelCase__      =							rag_model.rag.question_encoder(__lowerCamelCase       )
      UpperCamelCase__      =							question_enc_outputs[0]

      UpperCamelCase__      =							rag_model.retriever(
          __lowerCamelCase ,     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" ,     )
      UpperCamelCase__      =							rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
      UpperCamelCase__      =							[]
      for docs in all_docs:
            UpperCamelCase__      =							[strip_title(__lowerCamelCase       ) for title in docs["title"]]
            provenance_strings.append("\t".join(__lowerCamelCase       )       )
      return provenance_strings







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


      '''simple docstring'''



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

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

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

            return answers







def _UpperCamelCase			(	)				->						Any:


      '''simple docstring'''



      UpperCamelCase__      =							argparse.ArgumentParser()
      parser.add_argument(
          "--model_type" ,     choices=["rag_sequence", "rag_token", "bart"] ,     type=__lowerCamelCase ,     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=__lowerCamelCase ,     choices=["exact", "compressed", "legacy"] ,     type=__lowerCamelCase ,     help="RAG model retriever type" ,     )
      parser.add_argument(
          "--index_path" ,     default=__lowerCamelCase ,     type=__lowerCamelCase ,     help="Path to the retrieval index" ,     )
      parser.add_argument("--n_docs" ,     default=5 ,     type=__lowerCamelCase ,     help="Number of retrieved docs"       )
      parser.add_argument(
          "--model_name_or_path" ,     default=__lowerCamelCase ,     type=__lowerCamelCase ,     required=__lowerCamelCase ,     help="Path to pretrained checkpoints or model identifier from huggingface.co/models" ,     )
      parser.add_argument(
          "--eval_mode" ,     choices=["e2e", "retrieval"] ,     default="e2e" ,     type=__lowerCamelCase ,     help=(
              "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
              " precision@k."
          ) ,     )
      parser.add_argument("--k" ,     default=1 ,     type=__lowerCamelCase ,     help="k for the precision@k calculation"       )
      parser.add_argument(
          "--evaluation_set" ,     default=__lowerCamelCase ,     type=__lowerCamelCase ,     required=__lowerCamelCase ,     help="Path to a file containing evaluation samples" ,     )
      parser.add_argument(
          "--gold_data_path" ,     default=__lowerCamelCase ,     type=__lowerCamelCase ,     required=__lowerCamelCase ,     help="Path to a tab-separated file with gold samples" ,     )
      parser.add_argument(
          "--gold_data_mode" ,     default="qa" ,     type=__lowerCamelCase ,     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=__lowerCamelCase ,     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=__lowerCamelCase ,     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=__lowerCamelCase ,     help="Number of beams to be used when generating answers" ,     )
      parser.add_argument("--min_length" ,     default=1 ,     type=__lowerCamelCase ,     help="Min length of the generated answers"       )
      parser.add_argument("--max_length" ,     default=50 ,     type=__lowerCamelCase ,     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." ,     )
      UpperCamelCase__      =							parser.parse_args()
      UpperCamelCase__      =							torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
      return args







def _UpperCamelCase			(	__A       )				->						List[str]:


      '''simple docstring'''



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

      UpperCamelCase__      =							(
          [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" ,     __lowerCamelCase       )

      UpperCamelCase__      =							get_scores if args.eval_mode == "e2e" else get_precision_at_k
      UpperCamelCase__      =							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(__lowerCamelCase ,     args.predictions_path ,     args.gold_data_path       )
                  continue

            logger.info("***** Running evaluation for {} *****".format(__lowerCamelCase       )       )
            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"       ):
                  UpperCamelCase__      =							RagRetriever.from_pretrained(__lowerCamelCase ,     **__lowerCamelCase       )
                  UpperCamelCase__      =							model_class.from_pretrained(__lowerCamelCase ,     retriever=__lowerCamelCase ,     **__lowerCamelCase       )
                  model.retriever.init_retrieval()
            else:
                  UpperCamelCase__      =							model_class.from_pretrained(__lowerCamelCase ,     **__lowerCamelCase       )
            model.to(args.device       )

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

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


if __name__ == "__main__":
  a__    :   str								=			get_args()
  main(args)









'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					RePaintPipeline
     UpperCAmelCase			       =					IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     UpperCAmelCase			       =					IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase			       =					False






     def 			a_       (      self		)							->     int:
          torch.manual_seed(0		)
          torch.manual_seed(0		)
          _a            =		UNetaDModel(
              block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,				up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,				)
          _a            =		RePaintScheduler()
          _a            =		{"unet": unet, "scheduler": scheduler}
          return components






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Tuple:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		np.random.RandomState(__UpperCamelCase		).standard_normal((1, 3, 32, 32)		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		(image > 0).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		{
              "image": image,
              "mask_image": mask,
              "generator": generator,
              "num_inference_steps": 5,
              "eta": 0.0,
              "jump_length": 2,
              "jump_n_sample": 2,
              "output_type": "numpy",
          }
          return inputs






     def 			a_       (      self		)							->     Optional[Any]:
          _a            =		"cpu"  # ensure determinism for the device-dependent torch.Generator
          _a            =		self.get_dummy_components()
          _a            =		RePaintPipeline(**__UpperCamelCase		)
          _a            =		sd_pipe.to(__UpperCamelCase		)
          sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		self.get_dummy_inputs(__UpperCamelCase		)
          _a            =		sd_pipe(**__UpperCamelCase		).images
          _a            =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _a            =		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3






     @skip_mps
     def 			a_       (      self		)							->     Union[str, Any]:
          return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"		)
     def 			a_       (      self		)							->     int:
          return super().test_inference_batch_single_identical()






     @skip_mps
     def 			a_       (      self		)							->     str:
          return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 			a_       (      self		)							->     int:
          return super().test_save_load_optional_components()






     @skip_mps
     def 			a_       (      self		)							->     Optional[int]:
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Union[str, Any]:
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
          _a            =		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"		)

          _a            =		"google/ddpm-ema-celebahq-256"
          _a            =		UNetaDModel.from_pretrained(__UpperCamelCase		)
          _a            =		RePaintScheduler.from_pretrained(__UpperCamelCase		)

          _a            =		RePaintPipeline(unet=__UpperCamelCase  ,				scheduler=__UpperCamelCase		).to(__UpperCamelCase		)
          repaint.set_progress_bar_config(disable=__UpperCamelCase		)
          repaint.enable_attention_slicing()

          _a            =		torch.manual_seed(0		)
          _a            =		repaint(
              __UpperCamelCase  ,				__UpperCamelCase  ,				num_inference_steps=250  ,				eta=0.0  ,				jump_length=10  ,				jump_n_sample=10  ,				generator=__UpperCamelCase  ,				output_type="np"  ,				)
          _a            =		output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image		).mean() < 1e-2

