



"""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


__UpperCAmelCase      :      Any   					=					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def      A      (			_A							):




     """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      A      (			_A,     _A,     _A							):




     """simple docstring"""
     return max(metric_fn(snake_case__,     snake_case__							) for gt in ground_truths							)
def      A      (			_A,     _A,     _A							):




     """simple docstring"""
     snake_case_						:Dict								=						[line.strip() for line in open(snake_case__,     "r"							).readlines()]
     snake_case_						:Any								=						[]

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

     snake_case_						:List[Any]								=						0
     for prediction, ground_truths in zip(snake_case__,     snake_case__							):
          total += 1
          em += metric_max_over_ground_truths(snake_case__,     snake_case__,     snake_case__							)
          fa += metric_max_over_ground_truths(snake_case__,     snake_case__,     snake_case__							)

     snake_case_						:str								=						100.0 * em / total
     snake_case_						:int								=						100.0 * fa / total

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




     """simple docstring"""
     snake_case_						:List[Any]								=						args.k
     snake_case_						:List[Any]								=						[line.strip() for line in open(snake_case__,     "r"							).readlines()]
     snake_case_						:Optional[int]								=						[line.strip() for line in open(snake_case__,     "r"							).readlines()]

     snake_case_						:Union[str, Any]								=						0
     for hypo, reference in zip(snake_case__,     snake_case__							):
          snake_case_						:Tuple								=						set(hypo.split("\t"							)[:k]							)
          snake_case_						:List[str]								=						set(reference.split("\t"							)							)
          total += 1
          em += len(hypo_provenance & ref_provenance							) / k

     snake_case_						:Union[str, Any]								=						100.0 * em / total
     logger.info(F'''Precision@{k}: {em: .2f}'''							)
def      A      (			_A,     _A,     _A							):




     """simple docstring"""
     def strip_title(_A							):
          if title.startswith("\""							):
               snake_case_						:Optional[Any]								=						title[1:]
          if title.endswith("\""							):
               snake_case_						:Dict								=						title[:-1]
          return title

     snake_case_						:Union[str, Any]								=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         snake_case__,     return_tensors="pt",     padding=snake_case__,     truncation=snake_case__,     )["input_ids"].to(args.device							)

     snake_case_						:List[Any]								=						rag_model.rag.question_encoder(snake_case__							)
     snake_case_						:Tuple								=						question_enc_outputs[0]

     snake_case_						:str								=						rag_model.retriever(
         snake_case__,     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",     )
     snake_case_						:Dict								=						rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
     snake_case_						:List[str]								=						[]
     for docs in all_docs:
          snake_case_						:List[Any]								=						[strip_title(snake_case__							) for title in docs["title"]]
          provenance_strings.append("\t".join(snake_case__							)							)
     return provenance_strings
def      A      (			_A,     _A,     _A							):




     """simple docstring"""
     with torch.no_grad():
          snake_case_						:Any								=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              snake_case__,     return_tensors="pt",     padding=snake_case__,     truncation=snake_case__							)

          snake_case_						:Any								=						inputs_dict.input_ids.to(args.device							)
          snake_case_						:int								=						inputs_dict.attention_mask.to(args.device							)
          snake_case_						:str								=						rag_model.generate(  # rag_model overwrites generate
              snake_case__,     attention_mask=snake_case__,     num_beams=args.num_beams,     min_length=args.min_length,     max_length=args.max_length,     early_stopping=snake_case__,     num_return_sequences=1,     bad_words_ids=[[0, 0]],     )
          snake_case_						:Any								=						rag_model.retriever.generator_tokenizer.batch_decode(snake_case__,     skip_special_tokens=snake_case__							)

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

          return answers
def      A      (			):




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




     """simple docstring"""
     snake_case_						:Optional[int]								=						{}
     if args.model_type is None:
          snake_case_						:Tuple								=						infer_model_type(args.model_name_or_path							)
          assert args.model_type is not None
     if args.model_type.startswith("rag"							):
          snake_case_						:Union[str, Any]								=						RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
          snake_case_						:str								=						args.n_docs
          if args.index_name is not None:
               snake_case_						:Any								=						args.index_name
          if args.index_path is not None:
               snake_case_						:Optional[int]								=						args.index_path
     else:
          snake_case_						:Tuple								=						BartForConditionalGeneration

     snake_case_						:Dict								=						(
         [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",     snake_case__							)

     snake_case_						:List[Any]								=						get_scores if args.eval_mode == "e2e" else get_precision_at_k
     snake_case_						:int								=						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(snake_case__,     args.predictions_path,     args.gold_data_path							)
               continue

          logger.info("***** Running evaluation for {} *****".format(snake_case__							)							)
          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"							):
               snake_case_						:Any								=						RagRetriever.from_pretrained(snake_case__,     **snake_case__							)
               snake_case_						:Optional[int]								=						model_class.from_pretrained(snake_case__,     retriever=snake_case__,     **snake_case__							)
               model.retriever.init_retrieval()
          else:
               snake_case_						:List[Any]								=						model_class.from_pretrained(snake_case__,     **snake_case__							)
          model.to(args.device							)

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

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


if __name__ == "__main__":
    __UpperCAmelCase      :      Optional[int]   					=					get_args()
    main(args)






"""simple docstring"""






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     CONFIG_NAME







					@register_to_config
					def __init__(      self    ,       a = True    ,       a = 8    ,       a = "lanczos"    ,       a = True    ,       a = False    ,       ):




										"""simple docstring"""





										super().__init__()







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:Union[str, Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:List[str]								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:str								=						[Image.fromarray(a   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if not isinstance(a    ,       a   ):
															snake_case_						:Dict								=						[images]
										snake_case_						:Optional[int]								=						[np.array(a   ).astype(np.floataa   ) / 255.0 for image in images]
										snake_case_						:Optional[int]								=						np.stack(a    ,       axis=0   )

										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:int								=						images[..., None]

										snake_case_						:str								=						torch.from_numpy(images.transpose(0    ,       3    ,       1    ,       2   )   )
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:str								=						images.cpu().permute(0    ,       2    ,       3    ,       1   ).float().numpy()
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return 2.0 * images - 1.0







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return (images / 2 + 0.5).clamp(0    ,       1   )







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						image.convert("RGB"   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										if height is None:
															snake_case_						:Optional[Any]								=						image.height
										if width is None:
															snake_case_						:Dict								=						image.width

										snake_case_	,  snake_case_						:Optional[Any]								=						(
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										snake_case_						:Any								=						image.resize((width, height)    ,       resample=PIL_INTERPOLATION[self.config.resample]   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(a    ,       a   ):
															snake_case_						:str								=						[image]
										elif not (isinstance(a    ,       a   ) and all(isinstance(a    ,       a   ) for i in image   )):
															raise ValueError(
															    F'''Input is in incorrect format: {[type(a   ) for i in image]}. Currently, we only support {', '.join(a   )}'''   )

										if isinstance(image[0]    ,       PIL.Image.Image   ):
															if self.config.do_convert_rgb:
																				snake_case_						:int								=						[self.convert_to_rgb(a   ) for i in image]
															if self.config.do_resize:
																				snake_case_						:Tuple								=						[self.resize(a    ,       a    ,       a   ) for i in image]
															snake_case_						:Dict								=						self.pil_to_numpy(a   )  # to np
															snake_case_						:int								=						self.numpy_to_pt(a   )  # to pt

										elif isinstance(image[0]    ,       np.ndarray   ):
															snake_case_						:Optional[Any]								=						np.concatenate(a    ,       axis=0   ) if image[0].ndim == 4 else np.stack(a    ,       axis=0   )
															snake_case_						:List[Any]								=						self.numpy_to_pt(a   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[int]								=						image.shape
															if self.config.do_resize and (
															    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
															):
																				raise ValueError(
																				    F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
																				    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''   )

										elif isinstance(image[0]    ,       torch.Tensor   ):
															snake_case_						:Optional[Any]								=						torch.cat(a    ,       axis=0   ) if image[0].ndim == 4 else torch.stack(a    ,       axis=0   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:int								=						image.shape

															# don't need any preprocess if the image is latents
															if channel == 4:
																				return image

															if self.config.do_resize and (
															    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
															):
																				raise ValueError(
																				    F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
																				    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''   )

        # expected range [0,1], normalize to [-1,1]
										snake_case_						:Union[str, Any]								=						self.config.do_normalize
										if image.min() < 0:
															warnings.warn(
															    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
															    F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''    ,       a    ,       )
															snake_case_						:Optional[int]								=						False

										if do_normalize:
															snake_case_						:Tuple								=						self.normalize(a   )

										return image







					def 		_a (      self    ,       a    ,       a = "pil"    ,       a = None    ,       ):




										"""simple docstring"""





										if not isinstance(a    ,       torch.Tensor   ):
															raise ValueError(
															    F'''Input for postprocessing is in incorrect format: {type(a   )}. We only support pytorch tensor'''   )
										if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_						:List[Any]								=						(
															    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"    ,       "1.0.0"    ,       a    ,       standard_warn=a   )
															snake_case_						:Dict								=						"np"

										if output_type == "latent":
															return image

										if do_denormalize is None:
															snake_case_						:List[Any]								=						[self.config.do_normalize] * image.shape[0]

										snake_case_						:Any								=						torch.stack(
										    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

										if output_type == "pt":
															return image

										snake_case_						:int								=						self.pt_to_numpy(a   )

										if output_type == "np":
															return image

										if output_type == "pil":
															return self.numpy_to_pil(a   )



class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     CONFIG_NAME







					@register_to_config
					def __init__(      self    ,       a = True    ,       a = 8    ,       a = "lanczos"    ,       a = True    ,       ):




										"""simple docstring"""





										super().__init__()







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:List[Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:str								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:Any								=						[Image.fromarray(image[:, :, :3]   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return image[:, :, 1] * 2**8 + image[:, :, 2]







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:Optional[int]								=						images[None, ...]
										snake_case_						:Union[str, Any]								=						images[:, :, :, 3:]
										if images.shape[-1] == 6:
															snake_case_						:Tuple								=						(images_depth * 2_55).round().astype("uint8"   )
															snake_case_						:Optional[Any]								=						[
															    Image.fromarray(self.rgblike_to_depthmap(a   )    ,       mode="I;16"   ) for image_depth in images_depth
															]
										elif images.shape[-1] == 4:
															snake_case_						:Optional[Any]								=						(images_depth * 65535.0).astype(np.uintaa   )
															snake_case_						:List[Any]								=						[Image.fromarray(a    ,       mode="I;16"   ) for image_depth in images_depth]
										else:
															raise Exception("Not supported"   )

										return pil_images







					def 		_a (      self    ,       a    ,       a = "pil"    ,       a = None    ,       ):




										"""simple docstring"""





										if not isinstance(a    ,       torch.Tensor   ):
															raise ValueError(
															    F'''Input for postprocessing is in incorrect format: {type(a   )}. We only support pytorch tensor'''   )
										if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_						:Any								=						(
															    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"    ,       "1.0.0"    ,       a    ,       standard_warn=a   )
															snake_case_						:Optional[Any]								=						"np"

										if do_denormalize is None:
															snake_case_						:Any								=						[self.config.do_normalize] * image.shape[0]

										snake_case_						:Tuple								=						torch.stack(
										    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

										snake_case_						:List[str]								=						self.pt_to_numpy(a   )

										if output_type == "np":
															if image.shape[-1] == 6:
																				snake_case_						:Optional[int]								=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]    ,       axis=0   )
															else:
																				snake_case_						:Any								=						image[:, :, :, 3:]
															return image[:, :, :, :3], image_depth

										if output_type == "pil":
															return self.numpy_to_pil(a   ), self.numpy_to_depth(a   )
										else:
															raise Exception(F'''This type {output_type} is not supported'''   )

