



'''simple docstring'''

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:
				UpperCAmelCase     		=   False


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






def 		_snake_case		( )      ->						Optional[int]:







	"""simple docstring"""
	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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						List[str]:







	"""simple docstring"""
	requires_backends(_SCREAMING_SNAKE_CASE							,		["""torch"""]							)
	_check_torch_version()

	lowerCAmelCase   =     image_tensor.unsqueeze(0							)
	lowerCAmelCase   =     torch.nn.functional.unfold(_SCREAMING_SNAKE_CASE							,		(patch_height, patch_width)							,		stride=(patch_height, patch_width)							)
	lowerCAmelCase   =     patches.reshape(image_tensor.size(0							)							,		image_tensor.size(1							)							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		-1							)
	lowerCAmelCase   =     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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						int = 36							,		_SCREAMING_SNAKE_CASE					:						str = "black"							,		_SCREAMING_SNAKE_CASE					:						str = "white"							,		_SCREAMING_SNAKE_CASE					:						int = 5							,		_SCREAMING_SNAKE_CASE					:						int = 5							,		_SCREAMING_SNAKE_CASE					:						int = 5							,		_SCREAMING_SNAKE_CASE					:						int = 5							,		_SCREAMING_SNAKE_CASE					:						Optional[bytes] = None							,		_SCREAMING_SNAKE_CASE					:						Optional[str] = None							,		)      ->						Image.Image:







	"""simple docstring"""
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""vision"""							)
	# Add new lines so that each line is no more than 80 characters.

	lowerCAmelCase   =     textwrap.TextWrapper(width=80							)
	lowerCAmelCase   =     wrapper.wrap(text=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     """\n""".join(_SCREAMING_SNAKE_CASE							)

	if font_bytes is not None and font_path is None:
		lowerCAmelCase   =     io.BytesIO(_SCREAMING_SNAKE_CASE							)
	elif font_path is not None:
		lowerCAmelCase   =     font_path
	else:
		lowerCAmelCase   =     hf_hub_download(_SCREAMING_SNAKE_CASE							,		"""Arial.TTF"""							)
	lowerCAmelCase   =     ImageFont.truetype(_SCREAMING_SNAKE_CASE							,		encoding="""UTF-8"""							,		size=_SCREAMING_SNAKE_CASE							)

	# Use a temporary canvas to determine the width and height in pixels when
	# rendering the text.
	lowerCAmelCase   =     ImageDraw.Draw(Image.new("""RGB"""							,		(1, 1)							,		_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     temp_draw.textbbox((0, 0)							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# Create the actual image with a bit of padding around the text.
	lowerCAmelCase   =     text_width + left_padding + right_padding
	lowerCAmelCase   =     text_height + top_padding + bottom_padding
	lowerCAmelCase   =     Image.new("""RGB"""							,		(image_width, image_height)							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     ImageDraw.Draw(_SCREAMING_SNAKE_CASE							)
	draw.text(xy=(left_padding, top_padding)							,		text=_SCREAMING_SNAKE_CASE							,		fill=_SCREAMING_SNAKE_CASE							,		font=_SCREAMING_SNAKE_CASE							)
	return image






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						np.ndarray							,		_SCREAMING_SNAKE_CASE					:						str							,		**_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Tuple:







	"""simple docstring"""
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""vision"""							)

	# Convert to PIL image if necessary
	lowerCAmelCase   =     to_pil_image(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase   =     render_text(_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     max(header_image.width							,		image.width							)

	lowerCAmelCase   =     int(image.height * (new_width / image.width)							)
	lowerCAmelCase   =     int(header_image.height * (new_width / header_image.width)							)

	lowerCAmelCase   =     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
	lowerCAmelCase   =     to_numpy_array(_SCREAMING_SNAKE_CASE							)

	if infer_channel_dimension_format(_SCREAMING_SNAKE_CASE							) == ChannelDimension.LAST:
		lowerCAmelCase   =     to_channel_dimension_format(_SCREAMING_SNAKE_CASE							,		ChannelDimension.LAST							)

	return new_image





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[str]									=		["flattened_patches"]


						def __init__( self     ,	A_ = True     ,	A_ = True     ,	A_ = None     ,	A_ = 2048     ,	A_ = False     ,	**A_     ,	)     ->     None:
							super().__init__(**A_				)
							lowerCAmelCase   =     patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							lowerCAmelCase   =     do_normalize
							lowerCAmelCase   =     do_convert_rgb
							lowerCAmelCase   =     max_patches
							lowerCAmelCase   =     is_vqa


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	**A_				)     ->     np.ndarray:
							requires_backends(self.extract_flattened_patches     ,	"""torch"""				)
							_check_torch_version()

							# convert to torch
							lowerCAmelCase   =     to_channel_dimension_format(A_     ,	ChannelDimension.FIRST				)
							lowerCAmelCase   =     torch.from_numpy(A_				)

							lowerCAmelCase,       lowerCAmelCase   =     patch_size["""height"""], patch_size["""width"""]
							lowerCAmelCase,       lowerCAmelCase   =     get_image_size(A_				)

							# maximize scale s.t.
							lowerCAmelCase   =     math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
							lowerCAmelCase   =     max(min(math.floor(scale * image_height / patch_height				)     ,	A_				)     ,	1				)
							lowerCAmelCase   =     max(min(math.floor(scale * image_width / patch_width				)     ,	A_				)     ,	1				)
							lowerCAmelCase   =     max(num_feasible_rows * patch_height     ,	1				)
							lowerCAmelCase   =     max(num_feasible_cols * patch_width     ,	1				)

							lowerCAmelCase   =     torch.nn.functional.interpolate(
							    image.unsqueeze(0				)     ,	size=(resized_height, resized_width)     ,	mode="""bilinear"""     ,	align_corners=A_     ,	antialias=A_     ,	).squeeze(0				)

							# [1, rows, columns, patch_height * patch_width * image_channels]
							lowerCAmelCase   =     torch_extract_patches(A_     ,	A_     ,	A_				)

							lowerCAmelCase   =     patches.shape
							lowerCAmelCase   =     patches_shape[1]
							lowerCAmelCase   =     patches_shape[2]
							lowerCAmelCase   =     patches_shape[3]

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

							# [rows * columns, 1]
							lowerCAmelCase   =     torch.arange(A_				).reshape([rows, 1]				).repeat(1     ,	A_				).reshape([rows * columns, 1]				)
							lowerCAmelCase   =     torch.arange(A_				).reshape([1, columns]				).repeat(A_     ,	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]
							lowerCAmelCase   =     row_ids.to(torch.floataa				)
							lowerCAmelCase   =     col_ids.to(torch.floataa				)

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

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

							lowerCAmelCase   =     to_numpy_array(A_				)

							return result


						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	**A_				)     ->     np.ndarray:
							if image.dtype == np.uinta:
								lowerCAmelCase   =     image.astype(np.floataa				)

							# take mean across the whole `image`
							lowerCAmelCase   =     np.mean(A_				)
							lowerCAmelCase   =     np.std(A_				)
							lowerCAmelCase   =     max(A_     ,	1.0 / math.sqrt(np.prod(image.shape				)				)				)

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




						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = ChannelDimension.FIRST     ,	**A_     ,	)     ->     ImageInput:
							lowerCAmelCase   =     do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase   =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							lowerCAmelCase   =     patch_size if patch_size is not None else self.patch_size
							lowerCAmelCase   =     max_patches if max_patches is not None else self.max_patches
							lowerCAmelCase   =     self.is_vqa

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

							lowerCAmelCase   =     make_list_of_images(A_				)

							if not valid_images(A_				):
								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:
								lowerCAmelCase   =     [convert_to_rgb(A_				) for image in images]

							# All transformations expect numpy arrays.
							lowerCAmelCase   =     [to_numpy_array(A_				) for image in images]

							if is_vqa:
								if header_text is None:
									raise ValueError("""A header text must be provided for VQA models."""				)
								lowerCAmelCase   =     kwargs.pop("""font_bytes"""     ,	A_				)
								lowerCAmelCase   =     kwargs.pop("""font_path"""     ,	A_				)

								if isinstance(A_     ,	A_				):
									lowerCAmelCase   =     [header_text] * len(A_				)

								lowerCAmelCase   =     [
								    render_header(A_     ,	header_text[i]     ,	font_bytes=A_     ,	font_path=A_				)
								    for i, image in enumerate(A_				)
								]

							if do_normalize:
								lowerCAmelCase   =     [self.normalize(image=A_				) for image in images]

							# convert to torch tensor and permute
							lowerCAmelCase   =     [
							    self.extract_flattened_patches(image=A_     ,	max_patches=A_     ,	patch_size=A_				)
							    for image in images
							]

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

							lowerCAmelCase   =     BatchFeature(
							    data={"""flattened_patches""": images, """attention_mask""": attention_masks}     ,	tensor_type=A_				)

							return encoded_outputs




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

transformers_logging.set_verbosity_info()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[Any]:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	return max(metric_fn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) for gt in ground_truths							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     []

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

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

	lowerCAmelCase   =     100.0 * em / total
	lowerCAmelCase   =     100.0 * fa / total

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     args.k
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]

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

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Any:







	"""simple docstring"""
	def strip_title(_SCREAMING_SNAKE_CASE					:						Union[str, Any]							):
		if title.startswith("""\""""							):
			lowerCAmelCase   =     title[1:]
		if title.endswith("""\""""							):
			lowerCAmelCase   =     title[:-1]
		return title

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

	lowerCAmelCase   =     rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     question_enc_outputs[0]

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Tuple:







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

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

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

		return answers






def 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     argparse.ArgumentParser()
	parser.add_argument(
	    """--model_type"""							,		choices=["""rag_sequence""", """rag_token""", """bart"""]							,		type=_SCREAMING_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=_SCREAMING_SNAKE_CASE							,		choices=["""exact""", """compressed""", """legacy"""]							,		type=_SCREAMING_SNAKE_CASE							,		help="""RAG model retriever type"""							,		)
	parser.add_argument(
	    """--index_path"""							,		default=_SCREAMING_SNAKE_CASE							,		type=_SCREAMING_SNAKE_CASE							,		help="""Path to the retrieval index"""							,		)
	parser.add_argument("""--n_docs"""							,		default=5							,		type=_SCREAMING_SNAKE_CASE							,		help="""Number of retrieved docs"""							)
	parser.add_argument(
	    """--model_name_or_path"""							,		default=_SCREAMING_SNAKE_CASE							,		type=_SCREAMING_SNAKE_CASE							,		required=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE							,		help="""k for the precision@k calculation"""							)
	parser.add_argument(
	    """--evaluation_set"""							,		default=_SCREAMING_SNAKE_CASE							,		type=_SCREAMING_SNAKE_CASE							,		required=_SCREAMING_SNAKE_CASE							,		help="""Path to a file containing evaluation samples"""							,		)
	parser.add_argument(
	    """--gold_data_path"""							,		default=_SCREAMING_SNAKE_CASE							,		type=_SCREAMING_SNAKE_CASE							,		required=_SCREAMING_SNAKE_CASE							,		help="""Path to a tab-separated file with gold samples"""							,		)
	parser.add_argument(
	    """--gold_data_mode"""							,		default="""qa"""							,		type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE							,		help="""Number of beams to be used when generating answers"""							,		)
	parser.add_argument("""--min_length"""							,		default=1							,		type=_SCREAMING_SNAKE_CASE							,		help="""Min length of the generated answers"""							)
	parser.add_argument("""--max_length"""							,		default=50							,		type=_SCREAMING_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."""							,		)
	lowerCAmelCase   =     parser.parse_args()
	lowerCAmelCase   =     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)
	return args






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Optional[Any]:







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

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

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

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

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

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

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

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


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