


"""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


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

transformers_logging.set_verbosity_info()



def 			A__		(					_UpperCAmelCase    :      Optional[int]	)							->	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 			A__		(					_UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      str	)							->	Dict:

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      Union[str, Any]	)							->	List[str]:

	'''simple docstring'''


	snake_case__  :						Any		=      [line.strip() for line in open(_UpperCAmelCase				,    "r"	).readlines()]
	snake_case__  :						Optional[int]		=      []

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

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

	snake_case__  :						str		=      1_0_0.0 * em / total
	snake_case__  :						str		=      1_0_0.0 * fa / total

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



def 			A__		(					_UpperCAmelCase    :      List[str]				,    _UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      Union[str, Any]	)							->	Dict:

	'''simple docstring'''


	snake_case__  :						Union[str, Any]		=      args.k
	snake_case__  :						int		=      [line.strip() for line in open(_UpperCAmelCase				,    "r"	).readlines()]
	snake_case__  :						Union[str, Any]		=      [line.strip() for line in open(_UpperCAmelCase				,    "r"	).readlines()]

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

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



def 			A__		(					_UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      Union[str, Any]	)							->	Union[str, Any]:

	'''simple docstring'''


	def strip_title(_UpperCAmelCase    :      Any	):
		if title.startswith("\""	):
			snake_case__  :						int		=      title[1:]
		if title.endswith("\""	):
			snake_case__  :						str		=      title[:-1]
		return title

	snake_case__  :						Tuple		=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    _UpperCAmelCase				,    return_tensors="pt"				,    padding=_UpperCAmelCase				,    truncation=_UpperCAmelCase				,    )["input_ids"].to(args.device	)

	snake_case__  :						List[str]		=      rag_model.rag.question_encoder(_UpperCAmelCase	)
	snake_case__  :						int		=      question_enc_outputs[0]

	snake_case__  :						Optional[Any]		=      rag_model.retriever(
	    _UpperCAmelCase				,    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__  :						int		=      rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
	snake_case__  :						Optional[int]		=      []
	for docs in all_docs:
		snake_case__  :						Any		=      [strip_title(_UpperCAmelCase	) for title in docs["title"]]
		provenance_strings.append("\t".join(_UpperCAmelCase	)	)
	return provenance_strings



def 			A__		(					_UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      str				,    _UpperCAmelCase    :      List[Any]	)							->	Dict:

	'''simple docstring'''


	with torch.no_grad():
		snake_case__  :						Tuple		=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _UpperCAmelCase				,    return_tensors="pt"				,    padding=_UpperCAmelCase				,    truncation=_UpperCAmelCase	)

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

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

		return answers



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

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]	)							->	Any:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      {}
	if args.model_type is None:
		snake_case__  :						Any		=      infer_model_type(args.model_name_or_path	)
		assert args.model_type is not None
	if args.model_type.startswith("rag"	):
		snake_case__  :						Tuple		=      RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
		snake_case__  :						Any		=      args.n_docs
		if args.index_name is not None:
			snake_case__  :						Dict		=      args.index_name
		if args.index_path is not None:
			snake_case__  :						str		=      args.index_path
	else:
		snake_case__  :						List[Any]		=      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"				,    _UpperCAmelCase	)

	snake_case__  :						Any		=      get_scores if args.eval_mode == "e2e" else get_precision_at_k
	snake_case__  :						Tuple		=      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(_UpperCAmelCase				,    args.predictions_path				,    args.gold_data_path	)
			continue

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

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

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


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










import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__A       =False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,)
@require_vision
@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Any						=      PixaStructImageProcessor()
					__UpperCAmelCase       :	Any						=      TaTokenizer.from_pretrained('''t5-small'''					)

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(a_			,			a_					)

					processor.save_pretrained(self.tmpdirname					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).tokenizer




			def 					snake_case__	(  self				:					int			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).image_processor




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      [np.random.randint(2_55			,			size=(3, 30, 4_00)			,			dtype=np.uinta					)]

					__UpperCAmelCase       :	Any						=      [Image.fromarray(np.moveaxis(a_			,			0			,			-1					)					) for x in image_inputs]

					return image_inputs




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	Any						=      self.get_image_processor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	Dict						=      PixaStructProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.image_processor.to_json_string()			,			image_processor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.image_processor			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	List[Any]						=      image_processor(a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	Union[str, Any]						=      processor(images=a_			,			return_tensors='''np'''					)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2					)




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''

					__UpperCAmelCase       :	Any						=      processor(text=a_					)

					__UpperCAmelCase       :	Dict						=      tokenizer(a_			,			return_token_type_ids=a_			,			add_special_tokens=a_					)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	List[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_					)

					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					# test if it raises when no input is passed
					with pytest.raises(a_					):
							processor()




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	int						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	int						=      processor(text=a_			,			images=a_					)

					__UpperCAmelCase       :	Tuple						=      [5_12, 10_24, 20_48, 40_96]
					__UpperCAmelCase       :	Tuple						=      [7_70, 7_70, 7_70, 7_70]
					# with text
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)

					# without text input
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Any						=      processor(images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)




			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	List[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      self.get_image_processor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Dict						=      '''lower newer'''
					__UpperCAmelCase       :	List[str]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Optional[Any]						=      processor(text=a_			,			images=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					__UpperCAmelCase       :	Optional[int]						=      processor(text=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(list(inputs.keys()					)			,			['''input_ids''', '''attention_mask''']					)





