



import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


SCREAMING_SNAKE_CASE__       =      '''\

'''

SCREAMING_SNAKE_CASE__       =      '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

SCREAMING_SNAKE_CASE__       =      '''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,     _KWARGS_DESCRIPTION      )
class      __lowerCAmelCase     (		datasets.Metric      ):
					"""simple docstring"""






					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								return datasets.MetricInfo(
								    description=_DESCRIPTION				,					citation=_CITATION				,					inputs_description=_KWARGS_DESCRIPTION				,					features=datasets.Features(
								        {
								            'input_texts': datasets.Value('string'					),
								        }					)				,					reference_urls=['https://huggingface.co/docs/transformers/perplexity']				,					)



					def     _a							(     self					:       Dict				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       int = 16				,					_snake_case					:       bool = True				,					_snake_case					:       Dict=None					):






								"""simple docstring"""


								if device is not None:
											assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
											if device == "gpu":
														A__       =    'cuda'
								else:
											A__       =    'cuda' if torch.cuda.is_available() else 'cpu'

								A__       =    AutoModelForCausalLM.from_pretrained(_snake_case					)
								A__       =    model.to(_snake_case					)

								A__       =    AutoTokenizer.from_pretrained(_snake_case					)

								# if batch_size > 1 (which generally leads to padding being required), and
								# if there is not an already assigned pad_token, assign an existing
								# special token to also be the padding token
								if tokenizer.pad_token is None and batch_size > 1:
											A__       =    list(tokenizer.special_tokens_map_extended.values()					)
											# check that the model already has at least one special token defined
											assert (
											    len(_snake_case					) > 0
											), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
											# assign one of the special tokens to also be the pad token
											tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]}					)

								if add_start_token:
											# leave room for <BOS> token to be added:
											assert (
											    tokenizer.bos_token is not None
											), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
											A__       =    model.config.max_length - 1
								else:
											A__       =    model.config.max_length

								A__       =    tokenizer(
								    _snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					return_tensors='pt'				,					return_attention_mask=_snake_case				,					).to(_snake_case					)

								A__       =    encodings['input_ids']
								A__       =    encodings['attention_mask']

								# check that each input is long enough:
								if add_start_token:
											assert torch.all(torch.ge(attn_masks.sum(1					)				,					1					)					), "Each input text must be at least one token long."
								else:
											assert torch.all(
											    torch.ge(attn_masks.sum(1					)				,					2					)					), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

								A__       =    []
								A__       =    CrossEntropyLoss(reduction='none'					)

								for start_index in logging.tqdm(range(0				,					len(_snake_case					)				,					_snake_case					)					):
											A__       =    min(start_index + batch_size				,					len(_snake_case					)					)
											A__       =    encoded_texts[start_index:end_index]
											A__       =    attn_masks[start_index:end_index]

											if add_start_token:
														A__       =    torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0					)					).to(_snake_case					)
														A__       =    torch.cat([bos_tokens_tensor, encoded_batch]				,					dim=1					)
														A__       =    torch.cat(
														    [torch.ones(bos_tokens_tensor.size()				,					dtype=torch.intaa					).to(_snake_case					), attn_mask]				,					dim=1					)

											A__       =    encoded_batch

											with torch.no_grad():
														A__       =    model(_snake_case				,					attention_mask=_snake_case					).logits

											A__       =    out_logits[..., :-1, :].contiguous()
											A__       =    labels[..., 1:].contiguous()
											A__       =    attn_mask[..., 1:].contiguous()

											A__       =    torch.expa(
											    (loss_fct(shift_logits.transpose(1				,					2					)				,					_snake_case					) * shift_attention_mask_batch).sum(1					)
											    / shift_attention_mask_batch.sum(1					)					)

											ppls += perplexity_batch.tolist()

								return {"perplexities": ppls, "mean_perplexity": np.mean(_snake_case					)}









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


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

transformers_logging.set_verbosity_info()


def A						(       __UpperCamelCase					)						->      Tuple:
			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 ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return max(metric_fn(__UpperCamelCase ,	__UpperCamelCase					) for gt in ground_truths					)


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]
			A__       =    []

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

			A__       =    A__       =    A__       =    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					)

			A__       =    100.0 * em / total
			A__       =    100.0 * fa / total

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[int]:
			A__       =    args.k
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]

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

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			def strip_title(__UpperCamelCase					):
						if title.startswith('"'					):
									A__       =    title[1:]
						if title.endswith('"'					):
									A__       =    title[:-1]
						return title

			A__       =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    __UpperCamelCase ,	return_tensors='pt' ,	padding=__UpperCamelCase ,	truncation=__UpperCamelCase ,	)['input_ids'].to(args.device					)

			A__       =    rag_model.rag.question_encoder(__UpperCamelCase					)
			A__       =    question_enc_outputs[0]

			A__       =    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' ,	)
			A__       =    rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
			A__       =    []
			for docs in all_docs:
						A__       =    [strip_title(__UpperCamelCase					) for title in docs['title']]
						provenance_strings.append('\t'.join(__UpperCamelCase					)					)
			return provenance_strings


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[Any]:
			with torch.no_grad():
						A__       =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    __UpperCamelCase ,	return_tensors='pt' ,	padding=__UpperCamelCase ,	truncation=__UpperCamelCase					)

						A__       =    inputs_dict.input_ids.to(args.device					)
						A__       =    inputs_dict.attention_mask.to(args.device					)
						A__       =    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]] ,	)
						A__       =    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						(       )						->      Any:
			A__       =    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.' ,	)
			A__       =    parser.parse_args()
			A__       =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
			return args


def A						(       __UpperCamelCase					)						->      int:
			A__       =    {}
			if args.model_type is None:
						A__       =    infer_model_type(args.model_name_or_path					)
						assert args.model_type is not None
			if args.model_type.startswith('rag'					):
						A__       =    RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
						A__       =    args.n_docs
						if args.index_name is not None:
									A__       =    args.index_name
						if args.index_path is not None:
									A__       =    args.index_path
			else:
						A__       =    BartForConditionalGeneration

			A__       =    (
			    [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					)

			A__       =    get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			A__       =    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'					):
									A__       =    RagRetriever.from_pretrained(__UpperCamelCase ,	**__UpperCamelCase					)
									A__       =    model_class.from_pretrained(__UpperCamelCase ,	retriever=__UpperCamelCase ,	**__UpperCamelCase					)
									model.retriever.init_retrieval()
						else:
									A__       =    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:
									A__       =    []
									for line in tqdm(__UpperCamelCase					):
												questions.append(line.strip()					)
												if len(__UpperCamelCase					) == args.eval_batch_size:
															A__       =    evaluate_batch_fn(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)
															preds_file.write('\n'.join(__UpperCamelCase					) + '\n'					)
															preds_file.flush()
															A__       =    []
									if len(__UpperCamelCase					) > 0:
												A__       =    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__":
							SCREAMING_SNAKE_CASE__       =      get_args()
							main(args)




