
"""simple docstring"""






import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests





def __A						(   a_ :							Union[str, Any]		,		a_ :							str=None     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= None
		if token is not None:
				SCREAMING_SNAKE_CASE       :     Tuple				= {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

		SCREAMING_SNAKE_CASE       :     Optional[int]				= F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= requests.get(a_		,		headers=a_     ).json()
		SCREAMING_SNAKE_CASE       :     List[str]				= {}

		try:
				job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}     )
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= math.ceil((result['''total_count'''] - 1_00) / 1_00     )

				for i in range(a_     ):
						SCREAMING_SNAKE_CASE       :     int				= requests.get(url + F"&page={i + 2}"		,		headers=a_     ).json()
						job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}     )

				return job_links
		except Exception:
				print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"     )

		return {}





def __A						(   a_ :							Any		,		a_ :							Any=None     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[Any]				= None
		if token is not None:
				SCREAMING_SNAKE_CASE       :     List[str]				= {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

		SCREAMING_SNAKE_CASE       :     Any				= F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
		SCREAMING_SNAKE_CASE       :     Tuple				= requests.get(a_		,		headers=a_     ).json()
		SCREAMING_SNAKE_CASE       :     str				= {}

		try:
				artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}     )
				SCREAMING_SNAKE_CASE       :     List[str]				= math.ceil((result['''total_count'''] - 1_00) / 1_00     )

				for i in range(a_     ):
						SCREAMING_SNAKE_CASE       :     Optional[int]				= requests.get(url + F"&page={i + 2}"		,		headers=a_     ).json()
						artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}     )

				return artifacts
		except Exception:
				print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"     )

		return {}





def __A						(   a_ :							Union[str, Any]		,		a_ :							Union[str, Any]		,		a_ :							Optional[Any]		,		a_ :							List[str]     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= None
		if token is not None:
				SCREAMING_SNAKE_CASE       :     Any				= {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

		SCREAMING_SNAKE_CASE       :     Tuple				= requests.get(a_		,		headers=a_		,		allow_redirects=a_     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= result.headers['''Location''']
		SCREAMING_SNAKE_CASE       :     Tuple				= requests.get(a_		,		allow_redirects=a_     )
		SCREAMING_SNAKE_CASE       :     List[str]				= os.path.join(a_		,		F"{artifact_name}.zip"     )
		with open(a_		,		'''wb'''     ) as fp:
				fp.write(response.content     )





def __A						(   a_ :							List[str]		,		a_ :							Tuple=None     )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[Any]				= []
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []
		SCREAMING_SNAKE_CASE       :     Tuple				= None

		with zipfile.ZipFile(a_     ) as z:
				for filename in z.namelist():
						if not os.path.isdir(a_     ):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(a_     ) as f:
												for line in f:
														SCREAMING_SNAKE_CASE       :     str				= line.decode('''UTF-8'''     ).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		SCREAMING_SNAKE_CASE       :     Optional[int]				= line[: line.index(''': '''     )]
																		SCREAMING_SNAKE_CASE       :     Tuple				= line[line.index(''': '''     ) + len(''': '''     ) :]
																		errors.append([error_line, error]     )
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith('''FAILED '''     ):
																# `test` is the test method that failed
																SCREAMING_SNAKE_CASE       :     Optional[int]				= line[len('''FAILED '''     ) :]
																failed_tests.append(a_     )
														elif filename == "job_name.txt":
																SCREAMING_SNAKE_CASE       :     List[Any]				= line

		if len(a_     ) != len(a_     ):
				raise ValueError(
				    F"`errors` and `failed_tests` should have the same number of elements. Got {len(a_     )} for `errors` "
				    F"and {len(a_     )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
				    ''' problem.'''     )

		SCREAMING_SNAKE_CASE       :     Tuple				= None
		if job_name and job_links:
				SCREAMING_SNAKE_CASE       :     Tuple				= job_links.get(a_		,		a_     )

		# A list with elements of the form (line of error, error, failed test)
		SCREAMING_SNAKE_CASE       :     Dict				= [x + [y] + [job_link] for x, y in zip(a_		,		a_     )]

		return result





def __A						(   a_ :							List[str]		,		a_ :							Union[str, Any]=None     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= []

		SCREAMING_SNAKE_CASE       :     List[Any]				= [os.path.join(a_		,		a_     ) for p in os.listdir(a_     ) if p.endswith('''.zip'''     )]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(a_		,		job_links=a_     )     )

		return errors





def __A						(   a_ :							Optional[Any]		,		a_ :							Optional[Any]=None     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= Counter()
		counter.update([x[1] for x in logs]     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= counter.most_common()
		SCREAMING_SNAKE_CASE       :     Optional[int]				= {}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

		SCREAMING_SNAKE_CASE       :     List[Any]				= dict(sorted(r.items()		,		key=lambda a_     : item[1]["count"]		,		reverse=a_     )     )
		return r





def __A						(   a_ :							Dict     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= test.split('''::'''     )[0]
		if test.startswith('''tests/models/'''     ):
				SCREAMING_SNAKE_CASE       :     Tuple				= test.split('''/'''     )[2]
		else:
				SCREAMING_SNAKE_CASE       :     List[Any]				= None

		return test





def __A						(   a_ :							str		,		a_ :							Optional[Any]=None     )->							Union[str, Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= [(x[0], x[1], get_model(x[2]     )) for x in logs]
		SCREAMING_SNAKE_CASE       :     Any				= [x for x in logs if x[2] is not None]
		SCREAMING_SNAKE_CASE       :     Dict				= {x[2] for x in logs}

		SCREAMING_SNAKE_CASE       :     List[str]				= {}
		for test in tests:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]     )
				SCREAMING_SNAKE_CASE       :     Tuple				= counter.most_common()
				SCREAMING_SNAKE_CASE       :     List[Any]				= {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				SCREAMING_SNAKE_CASE       :     List[str]				= sum(error_counts.values()     )
				if n_errors > 0:
						SCREAMING_SNAKE_CASE       :     List[Any]				= {'''count''': n_errors, '''errors''': error_counts}

		SCREAMING_SNAKE_CASE       :     Tuple				= dict(sorted(r.items()		,		key=lambda a_     : item[1]["count"]		,		reverse=a_     )     )
		return r





def __A						(   a_ :							Dict     )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= '''| no. | error | status |'''
		SCREAMING_SNAKE_CASE       :     str				= '''|-:|:-|:-|'''
		SCREAMING_SNAKE_CASE       :     Any				= [header, sep]
		for error in reduced_by_error:
				SCREAMING_SNAKE_CASE       :     List[Any]				= reduced_by_error[error]['''count''']
				SCREAMING_SNAKE_CASE       :     Dict				= F"| {count} | {error[:1_00]} |  |"
				lines.append(a_     )

		return "\n".join(a_     )





def __A						(   a_ :							Tuple     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= '''| model | no. of errors | major error | count |'''
		SCREAMING_SNAKE_CASE       :     Optional[int]				= '''|-:|-:|-:|-:|'''
		SCREAMING_SNAKE_CASE       :     List[str]				= [header, sep]
		for model in reduced_by_model:
				SCREAMING_SNAKE_CASE       :     Dict				= reduced_by_model[model]['''count''']
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= list(reduced_by_model[model]['''errors'''].items()     )[0]
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= F"| {model} | {count} | {error[:60]} | {_count} |"
				lines.append(a_     )

		return "\n".join(a_     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[Any]						     =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
					parser.add_argument(
					    "--output_dir",
					    type=str,
					    required=True,
					    help="Where to store the downloaded artifacts and other result files.",
					)
					parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
					lowerCamelCase__ :       Dict						     =			parser.parse_args()

					os.makedirs(args.output_dir, exist_ok=True)

					lowerCamelCase__ :       List[Any]						     =			get_job_links(args.workflow_run_id, token=args.token)
					lowerCamelCase__ :       Union[str, Any]						     =			{}
					# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
					# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
					if _job_links:
										for k, v in _job_links.items():
															# This is how GitHub actions combine job names.
															if " / " in k:
																				lowerCamelCase__ :       Dict						     =			k.find(" / ")
																				lowerCamelCase__ :       Any						     =			k[index + len(" / ") :]
															lowerCamelCase__ :       List[Any]						     =			v
					with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
										json.dump(job_links, fp, ensure_ascii=False, indent=4)

					lowerCamelCase__ :       Dict						     =			get_artifacts_links(args.workflow_run_id, token=args.token)
					with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
										json.dump(artifacts, fp, ensure_ascii=False, indent=4)

					for idx, (name, url) in enumerate(artifacts.items()):
										download_artifact(name, url, args.output_dir, args.token)
										# Be gentle to GitHub
										time.sleep(1)

					lowerCamelCase__ :       Optional[int]						     =			get_all_errors(args.output_dir, job_links=job_links)

					# `e[1]` is the error
					lowerCamelCase__ :       Optional[int]						     =			Counter()
					counter.update([e[1] for e in errors])

					# print the top 30 most common test errors
					lowerCamelCase__ :       int						     =			counter.most_common(30)
					for item in most_common:
										print(item)

					with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
										json.dump(errors, fp, ensure_ascii=False, indent=4)

					lowerCamelCase__ :       List[str]						     =			reduce_by_error(errors)
					lowerCamelCase__ :       Union[str, Any]						     =			reduce_by_model(errors)

					lowerCamelCase__ :       Any						     =			make_github_table(reduced_by_error)
					lowerCamelCase__ :       Any						     =			make_github_table_per_model(reduced_by_model)

					with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
										fp.write(sa)
					with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
										fp.write(sa)







"""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


lowerCamelCase__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''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						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






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





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

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

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for prediction, ground_truths in zip(a_		,		a_     ):
				total += 1
				em += metric_max_over_ground_truths(a_		,		a_		,		a_     )
				fa += metric_max_over_ground_truths(a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

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





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

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

		SCREAMING_SNAKE_CASE       :     Dict				= 100.0 * em / total
		logger.info(F"Precision@{k}: {em: .2f}"     )





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_		,		)['''input_ids'''].to(args.device     )

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever(
		    a_		,		question_enc_pool_output.cpu().detach().to(torch.floataa     ).numpy()		,		prefix=rag_model.rag.generator.config.prefix		,		n_docs=rag_model.config.n_docs		,		return_tensors='''pt'''		,		)
		SCREAMING_SNAKE_CASE       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.generate(  # rag_model overwrites generate
				    a_		,		attention_mask=a_		,		num_beams=args.num_beams		,		min_length=args.min_length		,		max_length=args.max_length		,		early_stopping=a_		,		num_return_sequences=1		,		bad_words_ids=[[0, 0]]		,		)
				SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever.generator_tokenizer.batch_decode(a_		,		skip_special_tokens=a_     )

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

				return answers





def __A						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''		,		choices=['''rag_sequence''', '''rag_token''', '''bart''']		,		type=a_		,		help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )		,		)
		parser.add_argument(
		    '''--index_name'''		,		default=a_		,		choices=['''exact''', '''compressed''', '''legacy''']		,		type=a_		,		help='''RAG model retriever type'''		,		)
		parser.add_argument(
		    '''--index_path'''		,		default=a_		,		type=a_		,		help='''Path to the retrieval index'''		,		)
		parser.add_argument('''--n_docs'''		,		default=5		,		type=a_		,		help='''Number of retrieved docs'''     )
		parser.add_argument(
		    '''--model_name_or_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''		,		)
		parser.add_argument(
		    '''--eval_mode'''		,		choices=['''e2e''', '''retrieval''']		,		default='''e2e'''		,		type=a_		,		help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )		,		)
		parser.add_argument('''--k'''		,		default=1		,		type=a_		,		help='''k for the precision@k calculation'''     )
		parser.add_argument(
		    '''--evaluation_set'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a file containing evaluation samples'''		,		)
		parser.add_argument(
		    '''--gold_data_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a tab-separated file with gold samples'''		,		)
		parser.add_argument(
		    '''--gold_data_mode'''		,		default='''qa'''		,		type=a_		,		choices=['''qa''', '''ans''']		,		help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )		,		)
		parser.add_argument(
		    '''--predictions_path'''		,		type=a_		,		default='''predictions.txt'''		,		help='''Name of the predictions file, to be stored in the checkpoints directory'''		,		)
		parser.add_argument(
		    '''--eval_all_checkpoints'''		,		action='''store_true'''		,		help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''		,		)
		parser.add_argument(
		    '''--eval_batch_size'''		,		default=8		,		type=a_		,		help='''Batch size per GPU/CPU for evaluation.'''		,		)
		parser.add_argument(
		    '''--recalculate'''		,		help='''Recalculate predictions even if the prediction file exists'''		,		action='''store_true'''		,		)
		parser.add_argument(
		    '''--num_beams'''		,		default=4		,		type=a_		,		help='''Number of beams to be used when generating answers'''		,		)
		parser.add_argument('''--min_length'''		,		default=1		,		type=a_		,		help='''Min length of the generated answers'''     )
		parser.add_argument('''--max_length'''		,		default=50		,		type=a_		,		help='''Max length of the generated answers'''     )

		parser.add_argument(
		    '''--print_predictions'''		,		action='''store_true'''		,		help='''If True, prints predictions while evaluating.'''		,		)
		parser.add_argument(
		    '''--print_docs'''		,		action='''store_true'''		,		help='''If True, prints docs retried while generating.'''		,		)
		SCREAMING_SNAKE_CASE       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= infer_model_type(args.model_name_or_path     )
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= args.n_docs
				if args.index_name is not None:
						SCREAMING_SNAKE_CASE       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     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'''		,		a_     )

		SCREAMING_SNAKE_CASE       :     int				= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

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

				logger.info('''***** Running evaluation for {} *****'''.format(a_     )     )
				logger.info('''  Batch size = %d'''		,		args.eval_batch_size     )
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path     )     )

				if args.model_type.startswith('''rag'''     ):
						SCREAMING_SNAKE_CASE       :     Dict				= RagRetriever.from_pretrained(a_		,		**a_     )
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_class.from_pretrained(a_		,		retriever=a_		,		**a_     )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE       :     str				= model_class.from_pretrained(a_		,		**a_     )
				model.to(args.device     )

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

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


if __name__ == "__main__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





