import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
   import textwrap

   from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
   import torch

   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   a_    					=       False


a_    					=       logging.get_logger(__name__)
a_    					=       'ybelkada/fonts'





def    __lowercase     (						):
 if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
  raise ImportError(
      F"You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use "
      'Pix2StructImageProcessor. Please upgrade torch.'	)





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     Union[str, Any]	):
 requires_backends(lowerCamelCase  ,  ['torch']	)
 _check_torch_version()

 UpperCamelCase_					:						Optional[int] =					image_tensor.unsqueeze(0	)
 UpperCamelCase_					:						List[str] =					torch.nn.functional.unfold(lowerCamelCase  ,  (patch_height, patch_width)  ,  stride=(patch_height, patch_width)	)
 UpperCamelCase_					:						Union[str, Any] =					patches.reshape(image_tensor.size(0	)  ,  image_tensor.size(1	)  ,  lowerCamelCase  ,  lowerCamelCase  ,  -1	)
 UpperCamelCase_					:						List[Any] =					patches.permute(0  ,  4  ,  2  ,  3  ,  1	).reshape(
     image_tensor.size(2	) // patch_height  ,  image_tensor.size(3	) // patch_width  ,  image_tensor.size(1	) * patch_height * patch_width  ,  )
 return patches.unsqueeze(0	)





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     int = 36  ,  lowerCamelCase					:     str = "black"  ,  lowerCamelCase					:     str = "white"  ,  lowerCamelCase					:     int = 5  ,  lowerCamelCase					:     int = 5  ,  lowerCamelCase					:     int = 5  ,  lowerCamelCase					:     int = 5  ,  lowerCamelCase					:     Optional[bytes] = None  ,  lowerCamelCase					:     Optional[str] = None  ,  ):
 requires_backends(lowerCamelCase  ,  'vision'	)
 # Add new lines so that each line is no more than 80 characters.

 UpperCamelCase_					:						Dict =					textwrap.TextWrapper(width=80	)
 UpperCamelCase_					:						List[str] =					wrapper.wrap(text=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					'\n'.join(lowerCamelCase	)

 if font_bytes is not None and font_path is None:
  UpperCamelCase_					:						Any =					io.BytesIO(lowerCamelCase	)
 elif font_path is not None:
  UpperCamelCase_					:						str =					font_path
 else:
  UpperCamelCase_					:						Any =					hf_hub_download(lowerCamelCase  ,  'Arial.TTF'	)
 UpperCamelCase_					:						Optional[int] =					ImageFont.truetype(lowerCamelCase  ,  encoding='UTF-8'  ,  size=lowerCamelCase	)

 # Use a temporary canvas to determine the width and height in pixels when
 # rendering the text.
 UpperCamelCase_					:						int =					ImageDraw.Draw(Image.new('RGB'  ,  (1, 1)  ,  lowerCamelCase	)	)
 UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					temp_draw.textbbox((0, 0)  ,  lowerCamelCase  ,  lowerCamelCase	)

 # Create the actual image with a bit of padding around the text.
 UpperCamelCase_					:						Optional[Any] =					text_width + left_padding + right_padding
 UpperCamelCase_					:						Tuple =					text_height + top_padding + bottom_padding
 UpperCamelCase_					:						Union[str, Any] =					Image.new('RGB'  ,  (image_width, image_height)  ,  lowerCamelCase	)
 UpperCamelCase_					:						List[Any] =					ImageDraw.Draw(lowerCamelCase	)
 draw.text(xy=(left_padding, top_padding)  ,  text=lowerCamelCase  ,  fill=lowerCamelCase  ,  font=lowerCamelCase	)
 return image





def    __lowercase     (						lowerCamelCase					:     np.ndarray  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     Dict	):
 requires_backends(lowerCamelCase  ,  'vision'	)

 # Convert to PIL image if necessary
 UpperCamelCase_					:						Tuple =					to_pil_image(lowerCamelCase	)

 UpperCamelCase_					:						Union[str, Any] =					render_text(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						List[Any] =					max(header_image.width  ,  image.width	)

 UpperCamelCase_					:						Any =					int(image.height * (new_width / image.width)	)
 UpperCamelCase_					:						Union[str, Any] =					int(header_image.height * (new_width / header_image.width)	)

 UpperCamelCase_					:						List[Any] =					Image.new('RGB'  ,  (new_width, new_height + new_header_height)  ,  'white'	)
 new_image.paste(header_image.resize((new_width, new_header_height)	)  ,  (0, 0)	)
 new_image.paste(image.resize((new_width, new_height)	)  ,  (0, new_header_height)	)

 # Convert back to the original framework if necessary
 UpperCamelCase_					:						Optional[Any] =					to_numpy_array(lowerCamelCase	)

 if infer_channel_dimension_format(lowerCamelCase	) == ChannelDimension.LAST:
  UpperCamelCase_					:						int =					to_channel_dimension_format(lowerCamelCase  ,  ChannelDimension.LAST	)

 return new_image






class 		_lowercase						(					snake_case_       ):
 lowercase				     =   ['flattened_patches']

 def __init__( self	:							int   ,      snake_case	:							bool = True   ,      snake_case	:							bool = True   ,      snake_case	:							Dict[str, int] = None   ,      snake_case	:							int = 2_0_4_8   ,      snake_case	:							bool = False   ,      **snake_case	:							str   ,      )   ->						None:

  """simple docstring"""





  super().__init__(**snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
  UpperCamelCase_					:						Dict =					do_normalize
  UpperCamelCase_					:						int =					do_convert_rgb
  UpperCamelCase_					:						List[str] =					max_patches
  UpperCamelCase_					:						List[str] =					is_vqa

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							np.ndarray   ,      snake_case	:							int   ,      snake_case	:							dict   ,      **snake_case	:							Dict			)   ->						np.ndarray:

  """simple docstring"""





  requires_backends(self.extract_flattened_patches   ,      'torch'			)
  _check_torch_version()

  # convert to torch
  UpperCamelCase_					:						Optional[Any] =					to_channel_dimension_format(snake_case   ,      ChannelDimension.FIRST			)
  UpperCamelCase_					:						str =					torch.from_numpy(snake_case			)

  UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					patch_size['height'], patch_size['width']
  UpperCamelCase_, UpperCamelCase_					:						List[str] =					get_image_size(snake_case			)

  # maximize scale s.t.
  UpperCamelCase_					:						Any =					math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)			)
  UpperCamelCase_					:						Optional[int] =					max(min(math.floor(scale * image_height / patch_height			)   ,      snake_case			)   ,      1			)
  UpperCamelCase_					:						str =					max(min(math.floor(scale * image_width / patch_width			)   ,      snake_case			)   ,      1			)
  UpperCamelCase_					:						List[Any] =					max(num_feasible_rows * patch_height   ,      1			)
  UpperCamelCase_					:						List[str] =					max(num_feasible_cols * patch_width   ,      1			)

  UpperCamelCase_					:						Any =					torch.nn.functional.interpolate(
      image.unsqueeze(0			)   ,      size=(resized_height, resized_width)   ,      mode='bilinear'   ,      align_corners=snake_case   ,      antialias=snake_case   ,      ).squeeze(0			)

  # [1, rows, columns, patch_height * patch_width * image_channels]
  UpperCamelCase_					:						Union[str, Any] =					torch_extract_patches(snake_case   ,      snake_case   ,      snake_case			)

  UpperCamelCase_					:						str =					patches.shape
  UpperCamelCase_					:						List[Any] =					patches_shape[1]
  UpperCamelCase_					:						Union[str, Any] =					patches_shape[2]
  UpperCamelCase_					:						Optional[Any] =					patches_shape[3]

  # [rows * columns, patch_height * patch_width * image_channels]
  UpperCamelCase_					:						Tuple =					patches.reshape([rows * columns, depth]			)

  # [rows * columns, 1]
  UpperCamelCase_					:						Union[str, Any] =					torch.arange(snake_case			).reshape([rows, 1]			).repeat(1   ,      snake_case			).reshape([rows * columns, 1]			)
  UpperCamelCase_					:						List[Any] =					torch.arange(snake_case			).reshape([1, columns]			).repeat(snake_case   ,      1			).reshape([rows * columns, 1]			)

  # Offset by 1 so the ids do not contain zeros, which represent padding.
  row_ids += 1
  col_ids += 1

  # Prepare additional patch features.
  # [rows * columns, 1]
  UpperCamelCase_					:						Any =					row_ids.to(torch.floataa			)
  UpperCamelCase_					:						Any =					col_ids.to(torch.floataa			)

  # [rows * columns, 2 + patch_height * patch_width * image_channels]
  UpperCamelCase_					:						Dict =					torch.cat([row_ids, col_ids, patches]   ,      -1			)

  # [max_patches, 2 + patch_height * patch_width * image_channels]
  UpperCamelCase_					:						Tuple =					torch.nn.functional.pad(snake_case   ,      [0, 0, 0, max_patches - (rows * columns)]			).float()

  UpperCamelCase_					:						List[Any] =					to_numpy_array(snake_case			)

  return result

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							np.ndarray   ,      snake_case	:							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case	:							Dict			)   ->						np.ndarray:

  """simple docstring"""





  if image.dtype == np.uinta:
   UpperCamelCase_					:						int =					image.astype(np.floataa			)

  # take mean across the whole `image`
  UpperCamelCase_					:						Optional[Any] =					np.mean(snake_case			)
  UpperCamelCase_					:						List[str] =					np.std(snake_case			)
  UpperCamelCase_					:						Any =					max(snake_case   ,      1.0 / math.sqrt(np.prod(image.shape			)			)			)

  return normalize(snake_case   ,      mean=snake_case   ,      std=snake_case   ,      **snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							ImageInput   ,      snake_case	:							Optional[str] = None   ,      snake_case	:							bool = None   ,      snake_case	:							Optional[bool] = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[Dict[str, int]] = None   ,      snake_case	:							Optional[Union[str, TensorType]] = None   ,      snake_case	:							ChannelDimension = ChannelDimension.FIRST   ,      **snake_case	:							str   ,      )   ->						ImageInput:

  """simple docstring"""





  UpperCamelCase_					:						int =					do_normalize if do_normalize is not None else self.do_normalize
  UpperCamelCase_					:						List[str] =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  UpperCamelCase_					:						Any =					patch_size if patch_size is not None else self.patch_size
  UpperCamelCase_					:						List[Any] =					max_patches if max_patches is not None else self.max_patches
  UpperCamelCase_					:						Optional[Any] =					self.is_vqa

  if kwargs.get('data_format'   ,      snake_case			) is not None:
   raise ValueError('data_format is not an accepted input as the outputs are '			)

  UpperCamelCase_					:						List[str] =					make_list_of_images(snake_case			)

  if not valid_images(snake_case			):
   raise ValueError(
       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
       'torch.Tensor, tf.Tensor or jax.ndarray.'			)

  # PIL RGBA images are converted to RGB
  if do_convert_rgb:
   UpperCamelCase_					:						List[Any] =					[convert_to_rgb(snake_case			) for image in images]

  # All transformations expect numpy arrays.
  UpperCamelCase_					:						Union[str, Any] =					[to_numpy_array(snake_case			) for image in images]

  if is_vqa:
   if header_text is None:
    raise ValueError('A header text must be provided for VQA models.'			)
   UpperCamelCase_					:						Optional[int] =					kwargs.pop('font_bytes'   ,      snake_case			)
   UpperCamelCase_					:						List[str] =					kwargs.pop('font_path'   ,      snake_case			)

   if isinstance(snake_case   ,      snake_case			):
    UpperCamelCase_					:						Optional[int] =					[header_text] * len(snake_case			)

   UpperCamelCase_					:						Union[str, Any] =					[
       render_header(snake_case   ,      header_text[i]   ,      font_bytes=snake_case   ,      font_path=snake_case			)
       for i, image in enumerate(snake_case			)
   ]

  if do_normalize:
   UpperCamelCase_					:						Optional[int] =					[self.normalize(image=snake_case			) for image in images]

  # convert to torch tensor and permute
  UpperCamelCase_					:						List[str] =					[
      self.extract_flattened_patches(image=snake_case   ,      max_patches=snake_case   ,      patch_size=snake_case			)
      for image in images
  ]

  # create attention mask in numpy
  UpperCamelCase_					:						int =					[(image.sum(axis=-1			) != 0).astype(np.floataa			) for image in images]

  UpperCamelCase_					:						Optional[Any] =					BatchFeature(
      data={'flattened_patches': images, 'attention_mask': attention_masks}   ,      tensor_type=snake_case			)

  return encoded_outputs




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

transformers_logging.set_verbosity_info()





def    __lowercase     (						lowerCamelCase					:     Optional[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    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     str	):
 return max(metric_fn(lowerCamelCase  ,  lowerCamelCase	) for gt in ground_truths	)





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						Tuple =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]
 UpperCamelCase_					:						List[Any] =					[]

 if args.gold_data_mode == "qa":
  UpperCamelCase_					:						Union[str, Any] =					pd.read_csv(lowerCamelCase  ,  sep='\t'  ,  header=lowerCamelCase	)
  for answer_list in data[1]:
   UpperCamelCase_					:						Optional[int] =					ast.literal_eval(lowerCamelCase	)
   answers.append(lowerCamelCase	)
 else:
  UpperCamelCase_					:						int =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]
  UpperCamelCase_					:						Optional[int] =					[[reference] for reference in references]

 UpperCamelCase_					:						Optional[int] =					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_					:						Union[str, Any] =					1_0_0.0 * em / total
 UpperCamelCase_					:						List[Any] =					1_0_0.0 * fa / total

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





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						Optional[int] =					args.k
 UpperCamelCase_					:						List[Any] =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]
 UpperCamelCase_					:						List[str] =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]

 UpperCamelCase_					:						List[str] =					0
 for hypo, reference in zip(lowerCamelCase  ,  lowerCamelCase	):
  UpperCamelCase_					:						List[str] =					set(hypo.split('\t'	)[:k]	)
  UpperCamelCase_					:						int =					set(reference.split('\t'	)	)
  total += 1
  em += len(hypo_provenance & ref_provenance	) / k

 UpperCamelCase_					:						Union[str, Any] =					1_0_0.0 * em / total
 logger.info(F"Precision@{k}: {em: .2f}"	)





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     Any	):
 def strip_title(lowerCamelCase					:     List[str]	):
  if title.startswith('"'	):
   UpperCamelCase_					:						List[str] =					title[1:]
  if title.endswith('"'	):
   UpperCamelCase_					:						int =					title[:-1]
  return title

 UpperCamelCase_					:						Optional[int] =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
     lowerCamelCase  ,  return_tensors='pt'  ,  padding=lowerCamelCase  ,  truncation=lowerCamelCase  ,  )['input_ids'].to(args.device	)

 UpperCamelCase_					:						int =					rag_model.rag.question_encoder(lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					question_enc_outputs[0]

 UpperCamelCase_					:						Tuple =					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_					:						str =					rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
 UpperCamelCase_					:						int =					[]
 for docs in all_docs:
  UpperCamelCase_					:						Union[str, Any] =					[strip_title(lowerCamelCase	) for title in docs['title']]
  provenance_strings.append('\t'.join(lowerCamelCase	)	)
 return provenance_strings





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     List[Any]	):
 with torch.no_grad():
  UpperCamelCase_					:						List[Any] =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      lowerCamelCase  ,  return_tensors='pt'  ,  padding=lowerCamelCase  ,  truncation=lowerCamelCase	)

  UpperCamelCase_					:						Union[str, Any] =					inputs_dict.input_ids.to(args.device	)
  UpperCamelCase_					:						str =					inputs_dict.attention_mask.to(args.device	)
  UpperCamelCase_					:						List[Any] =					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_					:						str =					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    __lowercase     (						):
 UpperCamelCase_					:						Optional[int] =					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_					:						Union[str, Any] =					parser.parse_args()
 UpperCamelCase_					:						Union[str, Any] =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)
 return args





def    __lowercase     (						lowerCamelCase					:     int	):
 UpperCamelCase_					:						Any =					{}
 if args.model_type is None:
  UpperCamelCase_					:						List[Any] =					infer_model_type(args.model_name_or_path	)
  assert args.model_type is not None
 if args.model_type.startswith('rag'	):
  UpperCamelCase_					:						Optional[int] =					RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
  UpperCamelCase_					:						Dict =					args.n_docs
  if args.index_name is not None:
   UpperCamelCase_					:						Union[str, Any] =					args.index_name
  if args.index_path is not None:
   UpperCamelCase_					:						str =					args.index_path
 else:
  UpperCamelCase_					:						Tuple =					BartForConditionalGeneration

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



