




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowercase_		=							1000





class 		A__				:






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=1.2				,     lowerCamelCase="g"				,     lowerCamelCase=0.5				,     lowerCamelCase="-"				,     lowerCamelCase="test_out.jpg"				,     lowerCamelCase=True				,     lowerCamelCase=False				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=0.7				,     )      ->   Optional[Any]:




						"""simple docstring"""






						if isinstance(lowerCamelCase				,     torch.Tensor						):
											__magic_name__					:							int		   =							img.numpy().astype('''np.uint8'''						)
						if isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							Dict		   =							img_tensorize(lowerCamelCase						)
						assert isinstance(lowerCamelCase				,     np.ndarray						)

						__magic_name__   , __magic_name__					:							Optional[Any]		   =							img.shape[1], img.shape[0]
						__magic_name__					:							Tuple		   =							mplfigure.Figure(frameon=lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							fig.get_dpi()
						__magic_name__					:							str		   =							(width * scale + 1e-2) / dpi
						__magic_name__					:							List[str]		   =							(height * scale + 1e-2) / dpi
						fig.set_size_inches(lowerCamelCase				,     lowerCamelCase						)
						__magic_name__					:							List[str]		   =							fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
						ax.axis('''off'''						)
						ax.set_xlim(0.0				,     lowerCamelCase						)
						ax.set_ylim(lowerCamelCase						)

						__magic_name__					:							str		   =							saveas
						__magic_name__					:							Any		   =							rgb
						__magic_name__					:							Tuple		   =							pynb
						__magic_name__					:							Tuple		   =							img
						__magic_name__					:							Any		   =							edgecolor
						__magic_name__					:							Any		   =							0.5
						__magic_name__					:							Union[str, Any]		   =							linestyle
						__magic_name__					:							Any		   =							int(np.sqrt(min(lowerCamelCase				,     lowerCamelCase						)						) * scale // 3						)
						__magic_name__					:							Any		   =							width
						__magic_name__					:							str		   =							height
						__magic_name__					:							str		   =							scale
						__magic_name__					:							Optional[int]		   =							fig
						__magic_name__					:							List[Any]		   =							ax
						__magic_name__					:							int		   =							pad
						__magic_name__					:							int		   =							idaobj
						__magic_name__					:							Optional[Any]		   =							idaattr
						__magic_name__					:							List[Any]		   =							FigureCanvasAgg(lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   Any:




						"""simple docstring"""






						if color is None:
											__magic_name__					:							Dict		   =							self.edgecolor
						((__magic_name__)   , (__magic_name__)   , (__magic_name__)   , (__magic_name__))					:							Optional[int]		   =							box
						__magic_name__					:							Union[str, Any]		   =							xa - xa
						__magic_name__					:							Optional[int]		   =							ya - ya
						self.ax.add_patch(
						    mpl.patches.Rectangle(
						        (xa, ya)				,     lowerCamelCase				,     lowerCamelCase				,     fill=lowerCamelCase				,     edgecolor=lowerCamelCase				,     linewidth=self.font_size // 3				,     alpha=self.alpha				,     linestyle=self.linestyle				,     )						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None				,     lowerCamelCase=None						)      ->   str:




						"""simple docstring"""






						if len(boxes.shape						) > 2:
											__magic_name__					:							int		   =							boxes[0]
						if len(obj_ids.shape						) > 1:
											__magic_name__					:							Tuple		   =							obj_ids[0]
						if len(obj_scores.shape						) > 1:
											__magic_name__					:							List[str]		   =							obj_scores[0]
						if len(attr_ids.shape						) > 1:
											__magic_name__					:							Tuple		   =							attr_ids[0]
						if len(attr_scores.shape						) > 1:
											__magic_name__					:							int		   =							attr_scores[0]
						if isinstance(lowerCamelCase				,     torch.Tensor						):
											__magic_name__					:							Optional[int]		   =							boxes.numpy()
						if isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							List[Any]		   =							np.array(lowerCamelCase						)
						assert isinstance(lowerCamelCase				,     np.ndarray						)
						__magic_name__					:							Dict		   =							np.prod(boxes[:, 2:] - boxes[:, :2]				,     axis=1						)
						__magic_name__					:							Union[str, Any]		   =							np.argsort(-areas						).tolist()
						__magic_name__					:							int		   =							boxes[sorted_idxs] if boxes is not None else None
						__magic_name__					:							List[str]		   =							obj_ids[sorted_idxs] if obj_ids is not None else None
						__magic_name__					:							Optional[int]		   =							obj_scores[sorted_idxs] if obj_scores is not None else None
						__magic_name__					:							Tuple		   =							attr_ids[sorted_idxs] if attr_ids is not None else None
						__magic_name__					:							Dict		   =							attr_scores[sorted_idxs] if attr_scores is not None else None

						__magic_name__					:							str		   =							[self._random_color(maximum=1						) for _ in range(len(lowerCamelCase						)						)]
						__magic_name__					:							Tuple		   =							[assigned_colors[idx] for idx in sorted_idxs]
						if obj_ids is not None:
											__magic_name__					:							Union[str, Any]		   =							self._create_text_labels_attr(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)
											for i in range(len(lowerCamelCase						)						):
																__magic_name__					:							Union[str, Any]		   =							assigned_colors[i]
																self.add_box(boxes[i]				,     lowerCamelCase						)
																self.draw_labels(labels[i]				,     boxes[i]				,     lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__					:							Union[str, Any]		   =							box
						__magic_name__					:							Union[str, Any]		   =							(xa, ya)
						__magic_name__					:							int		   =							(ya - ya) * (xa - xa)
						__magic_name__					:							Tuple		   =							_SMALL_OBJ * self.scale
						if instance_area < small or ya - ya < 40 * self.scale:
											if ya >= self.height - 5:
																__magic_name__					:							Tuple		   =							(xa, ya)
											else:
																__magic_name__					:							List[Any]		   =							(xa, ya)

						__magic_name__					:							Optional[Any]		   =							(ya - ya) / np.sqrt(self.height * self.width						)
						__magic_name__					:							Optional[int]		   =							self._change_color_brightness(lowerCamelCase				,     brightness_factor=0.7						)
						__magic_name__					:							Union[str, Any]		   =							np.clip((height_ratio - 0.0_2) / 0.0_8 + 1				,     1.2				,     2						)
						font_size *= 0.7_5 * self.font_size

						self.draw_text(
						    text=lowerCamelCase				,     position=lowerCamelCase				,     color=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase="g"				,     lowerCamelCase="left"				,     )      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Any		   =							0
						__magic_name__					:							List[Any]		   =							self.font_size
						__magic_name__					:							List[Any]		   =							np.maximum(list(mplc.to_rgb(lowerCamelCase						)						)				,     0.2						)
						__magic_name__					:							Optional[Any]		   =							max(0.8				,     np.max(lowerCamelCase						)						)
						__magic_name__					:							Tuple		   =							{
						    '''facecolor''': '''black''',
						    '''alpha''': self.alpha,
						    '''pad''': self.pad,
						    '''edgecolor''': '''none''',
						}
						__magic_name__   , __magic_name__					:							str		   =							position
						self.ax.text(
						    lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     size=font_size * self.scale				,     family='''sans-serif'''				,     bbox=lowerCamelCase				,     verticalalignment='''top'''				,     horizontalalignment=lowerCamelCase				,     color=lowerCamelCase				,     zorder=10				,     rotation=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase=None						)      ->   Optional[Any]:




						"""simple docstring"""






						if saveas is None:
											__magic_name__					:							Dict		   =							self.saveas
						if saveas.lower().endswith('''.jpg'''						) or saveas.lower().endswith('''.png'''						):
											cva.imwrite(
											    lowerCamelCase				,     self._get_buffer()[:, :, ::-1]				,     )
						else:
											self.fig.savefig(lowerCamelCase						)






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




						"""simple docstring"""






						__magic_name__					:							Dict		   =							[self.idaobj[i] for i in classes]
						__magic_name__					:							int		   =							[self.idaattr[i] for i in attr_classes]
						__magic_name__					:							List[str]		   =							[
						    F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
						    for label, score, attr, attr_score in zip(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)
						]
						return labels






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							[self.idaobj[i] for i in classes]
						if scores is not None:
											if labels is None:
																__magic_name__					:							List[Any]		   =							['''{:.0f}%'''.format(s * 100						) for s in scores]
											else:
																__magic_name__					:							Union[str, Any]		   =							['''{} {:.0f}%'''.format(lowerCamelCase				,     s * 100						) for li, s in zip(lowerCamelCase				,     lowerCamelCase						)]
						return labels






	def  lowercase      (	self				,     lowerCamelCase=255						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							np.random.randint(0				,     len(_COLORS						)						)
						__magic_name__					:							int		   =							_COLORS[idx] * maximum
						if not self.rgb:
											__magic_name__					:							Optional[Any]		   =							ret[::-1]
						return ret






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




						"""simple docstring"""






						if not self.pynb:
											__magic_name__   , __magic_name__					:							int		   =							self.canvas.print_to_buffer()
											if (width, height) != (self.width, self.height):
																__magic_name__					:							List[Any]		   =							cva.resize(self.img				,     (width, height)						)
											else:
																__magic_name__					:							Union[str, Any]		   =							self.img
						else:
											__magic_name__					:							List[Any]		   =							io.BytesIO()  # works for cairo backend
											self.canvas.print_rgba(lowerCamelCase						)
											__magic_name__   , __magic_name__					:							List[str]		   =							self.width, self.height
											__magic_name__					:							int		   =							buf.getvalue()
											__magic_name__					:							Any		   =							self.img

						__magic_name__					:							Dict		   =							np.frombuffer(lowerCamelCase				,     dtype='''uint8'''						)
						__magic_name__					:							int		   =							buffer.reshape(lowerCamelCase				,     lowerCamelCase				,     4						)
						__magic_name__   , __magic_name__					:							Union[str, Any]		   =							np.split(lowerCamelCase				,     [3]				,     axis=2						)

						try:
											import numexpr as ne  # fuse them with numexpr

											__magic_name__					:							Union[str, Any]		   =							ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''						)
						except ImportError:
											__magic_name__					:							Optional[Any]		   =							alpha.astype('''float32'''						) / 2_5_5.0
											__magic_name__					:							Union[str, Any]		   =							img * (1 - alpha) + rgb * alpha

						return visualized_image.astype('''uint8'''						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						assert brightness_factor >= -1.0 and brightness_factor <= 1.0
						__magic_name__					:							Union[str, Any]		   =							mplc.to_rgb(lowerCamelCase						)
						__magic_name__					:							Dict		   =							colorsys.rgb_to_hls(*mplc.to_rgb(lowerCamelCase						)						)
						__magic_name__					:							Optional[Any]		   =							polygon_color[1] + (brightness_factor * polygon_color[1])
						__magic_name__					:							str		   =							0.0 if modified_lightness < 0.0 else modified_lightness
						__magic_name__					:							List[str]		   =							1.0 if modified_lightness > 1.0 else modified_lightness
						__magic_name__					:							Tuple		   =							colorsys.hls_to_rgb(polygon_color[0]				,     lowerCamelCase				,     polygon_color[2]						)
						return modified_color

lowercase_		=							(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






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

transformers_logging.set_verbosity_info()





def 	snake_case_      (__A     :						Any     )			->	Any:
   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 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]					,   __A     :						List[Any]     )			->	Dict:
   return max(metric_fn(__A					,   __A     ) for gt in ground_truths     )





def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						str					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   __lowerCAmelCase  :			int					    =						[line.strip() for line in open(__A					,   """r"""     ).readlines()]
   __lowerCAmelCase  :			str					    =						[]

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

   __lowerCAmelCase  :			int					    =						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  :			Any					    =						100.0 * em / total
   __lowerCAmelCase  :			Union[str, Any]					    =						100.0 * fa / total

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





def 	snake_case_      (__A     :						str					,   __A     :						Any					,   __A     :						Dict     )			->	Optional[Any]:
   __lowerCAmelCase  :			Optional[int]					    =						args.k
   __lowerCAmelCase  :			List[Any]					    =						[line.strip() for line in open(__A					,   """r"""     ).readlines()]
   __lowerCAmelCase  :			Any					    =						[line.strip() for line in open(__A					,   """r"""     ).readlines()]

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

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





def 	snake_case_      (__A     :						List[Any]					,   __A     :						Optional[Any]					,   __A     :						Dict     )			->	int:
   def strip_title(__A     :						List[Any]     ):
      if title.startswith("""\""""     ):
         __lowerCAmelCase  :			Dict					    =						title[1:]
      if title.endswith("""\""""     ):
         __lowerCAmelCase  :			Optional[int]					    =						title[:-1]
      return title

   __lowerCAmelCase  :			Dict					    =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
       __A					,   return_tensors="""pt"""					,   padding=__A					,   truncation=__A					,   )["""input_ids"""].to(args.device     )

   __lowerCAmelCase  :			Any					    =						rag_model.rag.question_encoder(__A     )
   __lowerCAmelCase  :			Optional[int]					    =						question_enc_outputs[0]

   __lowerCAmelCase  :			Dict					    =						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  :			int					    =						rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
   __lowerCAmelCase  :			List[str]					    =						[]
   for docs in all_docs:
      __lowerCAmelCase  :			List[str]					    =						[strip_title(__A     ) for title in docs["""title"""]]
      provenance_strings.append("""\t""".join(__A     )     )
   return provenance_strings





def 	snake_case_      (__A     :						Any					,   __A     :						Any					,   __A     :						List[str]     )			->	Tuple:
   with torch.no_grad():
      __lowerCAmelCase  :			Optional[Any]					    =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          __A					,   return_tensors="""pt"""					,   padding=__A					,   truncation=__A     )

      __lowerCAmelCase  :			Dict					    =						inputs_dict.input_ids.to(args.device     )
      __lowerCAmelCase  :			List[Any]					    =						inputs_dict.attention_mask.to(args.device     )
      __lowerCAmelCase  :			Optional[Any]					    =						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  :			Optional[int]					    =						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 	snake_case_      ()			->	int:
   __lowerCAmelCase  :			Optional[int]					    =						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=5_0					,   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  :			str					    =						parser.parse_args()
   __lowerCAmelCase  :			Dict					    =						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )
   return args





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

   __lowerCAmelCase  :			List[Any]					    =						(
       [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  :			str					    =						get_scores if args.eval_mode == """e2e""" else get_precision_at_k
   __lowerCAmelCase  :			str					    =						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  :			str					    =						RagRetriever.from_pretrained(__A					,   **__A     )
         __lowerCAmelCase  :			Optional[Any]					    =						model_class.from_pretrained(__A					,   retriever=__A					,   **__A     )
         model.retriever.init_retrieval()
      else:
         __lowerCAmelCase  :			int					    =						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  :			Optional[int]					    =						[]
         for line in tqdm(__A     ):
            questions.append(line.strip()     )
            if len(__A     ) == args.eval_batch_size:
               __lowerCAmelCase  :			Optional[int]					    =						evaluate_batch_fn(__A					,   __A					,   __A     )
               preds_file.write("""\n""".join(__A     ) + """\n"""     )
               preds_file.flush()
               __lowerCAmelCase  :			Dict					    =						[]
         if len(__A     ) > 0:
            __lowerCAmelCase  :			int					    =						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__":
   __UpperCAmelCase      = get_args()
   main(args)


