



'''simple docstring'''

import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


UpperCAmelCase     		=   (
    'https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'
)


UpperCAmelCase     		=   logging.get_logger(__name__)  # pylint: disable=invalid-name






def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     """https://pypi.org/pypi/diffusers/json"""
	lowerCAmelCase   =     json.loads(request.urlopen(_SCREAMING_SNAKE_CASE							).read()							)["""releases"""].keys()
	return sorted(_SCREAMING_SNAKE_CASE							,		key=lambda _SCREAMING_SNAKE_CASE							: version.Version(_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
	if HF_MODULES_CACHE in sys.path:
		return

	sys.path.append(_SCREAMING_SNAKE_CASE							)
	os.makedirs(_SCREAMING_SNAKE_CASE							,		exist_ok=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     Path(_SCREAMING_SNAKE_CASE							) / """__init__.py"""
	if not init_path.exists():
		init_path.touch()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, os.PathLike]							)      ->						Dict:







	"""simple docstring"""
	init_hf_modules()
	lowerCAmelCase   =     Path(_SCREAMING_SNAKE_CASE							) / name
	# If the parent module does not exist yet, recursively create it.
	if not dynamic_module_path.parent.exists():
		create_dynamic_module(dynamic_module_path.parent							)
	os.makedirs(_SCREAMING_SNAKE_CASE							,		exist_ok=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     dynamic_module_path / """__init__.py"""
	if not init_path.exists():
		init_path.touch()






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







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""r"""							,		encoding="""utf-8"""							) as f:
		lowerCAmelCase   =     f.read()

	# Imports of the form `import .xxx`
	lowerCAmelCase   =     re.findall("""^\s*import\s+\.(\S+)\s*$"""							,		_SCREAMING_SNAKE_CASE							,		flags=re.MULTILINE							)
	# Imports of the form `from .xxx import yyy`
	relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""							,		_SCREAMING_SNAKE_CASE							,		flags=re.MULTILINE							)
	# Unique-ify
	return list(set(_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Any:







	"""simple docstring"""
	lowerCAmelCase   =     False
	lowerCAmelCase   =     [module_file]
	lowerCAmelCase   =     []

	# Let's recurse through all relative imports
	while not no_change:
		lowerCAmelCase   =     []
		for f in files_to_check:
			new_imports.extend(get_relative_imports(_SCREAMING_SNAKE_CASE							)							)

		lowerCAmelCase   =     Path(_SCREAMING_SNAKE_CASE							).parent
		lowerCAmelCase   =     [str(module_path / m							) for m in new_imports]
		lowerCAmelCase   =     [f for f in new_import_files if f not in all_relative_imports]
		lowerCAmelCase   =     [f'{f}.py' for f in new_import_files]

		lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							) == 0
		all_relative_imports.extend(_SCREAMING_SNAKE_CASE							)

	return all_relative_imports






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







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""r"""							,		encoding="""utf-8"""							) as f:
		lowerCAmelCase   =     f.read()

	# Imports of the form `import xxx`
	lowerCAmelCase   =     re.findall("""^\s*import\s+(\S+)\s*$"""							,		_SCREAMING_SNAKE_CASE							,		flags=re.MULTILINE							)
	# Imports of the form `from xxx import yyy`
	imports += re.findall("""^\s*from\s+(\S+)\s+import"""							,		_SCREAMING_SNAKE_CASE							,		flags=re.MULTILINE							)
	# Only keep the top-level module
	lowerCAmelCase   =     [imp.split("""."""							)[0] for imp in imports if not imp.startswith("""."""							)]

	# Unique-ify and test we got them all
	lowerCAmelCase   =     list(set(_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase   =     []
	for imp in imports:
		try:
			importlib.import_module(_SCREAMING_SNAKE_CASE							)
		except ImportError:
			missing_packages.append(_SCREAMING_SNAKE_CASE							)

	if len(_SCREAMING_SNAKE_CASE							) > 0:
		raise ImportError(
		    """This modeling file requires the following packages that were not found in your environment: """
		    f'{", ".join(_SCREAMING_SNAKE_CASE							)}. Run `pip install {" ".join(_SCREAMING_SNAKE_CASE							)}`'							)

	return get_relative_imports(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Any							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     module_path.replace(os.path.sep							,		"""."""							)
	lowerCAmelCase   =     importlib.import_module(_SCREAMING_SNAKE_CASE							)

	if class_name is None:
		return find_pipeline_class(_SCREAMING_SNAKE_CASE							)
	return getattr(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






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







	"""simple docstring"""
	from ..pipelines import DiffusionPipeline

	lowerCAmelCase   =     dict(inspect.getmembers(_SCREAMING_SNAKE_CASE							,		inspect.isclass							)							)

	lowerCAmelCase   =     None
	for cls_name, cls in cls_members.items():
		if (
		    cls_name != DiffusionPipeline.__name__
		    and issubclass(cls							,		_SCREAMING_SNAKE_CASE							)
		    and cls.__module__.split("""."""							)[0] != "diffusers"
		):
			if pipeline_class is not None:
				raise ValueError(
				    f'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
				    f' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
				    f' {loaded_module}.'							)
			lowerCAmelCase   =     cls

	return pipeline_class






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, os.PathLike]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[Union[str, os.PathLike]] = None							,		_SCREAMING_SNAKE_CASE					:						bool = False							,		_SCREAMING_SNAKE_CASE					:						bool = False							,		_SCREAMING_SNAKE_CASE					:						Optional[Dict[str, str]] = None							,		_SCREAMING_SNAKE_CASE					:						Optional[Union[bool, str]] = None							,		_SCREAMING_SNAKE_CASE					:						Optional[str] = None							,		_SCREAMING_SNAKE_CASE					:						bool = False							,		)      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     str(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase   =     os.path.join(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	if os.path.isfile(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     module_file_or_url
		lowerCAmelCase   =     """local"""
	elif pretrained_model_name_or_path.count("""/"""							) == 0:
		lowerCAmelCase   =     get_diffusers_versions()
		# cut ".dev0"
		lowerCAmelCase   =     """v""" + """.""".join(__version__.split("""."""							)[:3]							)

		# retrieve github version that matches
		if revision is None:
			lowerCAmelCase   =     latest_version if latest_version[1:] in available_versions else """main"""
			logger.info(f'Defaulting to latest_version: {revision}.'							)
		elif revision in available_versions:
			lowerCAmelCase   =     f'v{revision}'
		elif revision == "main":
			lowerCAmelCase   =     revision
		else:
			raise ValueError(
			    f'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
			    f' {", ".join(available_versions + ["main"]							)}.'							)

		# community pipeline on GitHub
		lowerCAmelCase   =     COMMUNITY_PIPELINES_URL.format(revision=_SCREAMING_SNAKE_CASE							,		pipeline=_SCREAMING_SNAKE_CASE							)
		try:
			lowerCAmelCase   =     cached_download(
			    _SCREAMING_SNAKE_CASE							,		cache_dir=_SCREAMING_SNAKE_CASE							,		force_download=_SCREAMING_SNAKE_CASE							,		proxies=_SCREAMING_SNAKE_CASE							,		resume_download=_SCREAMING_SNAKE_CASE							,		local_files_only=_SCREAMING_SNAKE_CASE							,		use_auth_token=_SCREAMING_SNAKE_CASE							,		)
			lowerCAmelCase   =     """git"""
			lowerCAmelCase   =     pretrained_model_name_or_path + """.py"""
		except EnvironmentError:
			logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'							)
			raise
	else:
		try:
			# Load from URL or cache if already cached
			lowerCAmelCase   =     hf_hub_download(
			    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		cache_dir=_SCREAMING_SNAKE_CASE							,		force_download=_SCREAMING_SNAKE_CASE							,		proxies=_SCREAMING_SNAKE_CASE							,		resume_download=_SCREAMING_SNAKE_CASE							,		local_files_only=_SCREAMING_SNAKE_CASE							,		use_auth_token=_SCREAMING_SNAKE_CASE							,		)
			lowerCAmelCase   =     os.path.join("""local"""							,		"""--""".join(pretrained_model_name_or_path.split("""/"""							)							)							)
		except EnvironmentError:
			logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'							)
			raise

    # Check we have all the requirements in our environment
	lowerCAmelCase   =     check_imports(_SCREAMING_SNAKE_CASE							)

	# Now we move the module inside our cached dynamic modules.
	lowerCAmelCase   =     DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
	create_dynamic_module(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     Path(_SCREAMING_SNAKE_CASE							) / full_submodule
	if submodule == "local" or submodule == "git":
		# We always copy local files (we could hash the file to see if there was a change, and give them the name of
		# that hash, to only copy when there is a modification but it seems overkill for now).
		# The only reason we do the copy is to avoid putting too many folders in sys.path.
		shutil.copy(_SCREAMING_SNAKE_CASE							,		submodule_path / module_file							)
		for module_needed in modules_needed:
			lowerCAmelCase   =     f'{module_needed}.py'
			shutil.copy(os.path.join(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							,		submodule_path / module_needed							)
	else:
		# Get the commit hash
		# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
		if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
			lowerCAmelCase   =     use_auth_token
		elif use_auth_token is True:
			lowerCAmelCase   =     HfFolder.get_token()
		else:
			lowerCAmelCase   =     None

		lowerCAmelCase   =     model_info(_SCREAMING_SNAKE_CASE							,		revision=_SCREAMING_SNAKE_CASE							,		token=_SCREAMING_SNAKE_CASE							).sha

		# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
		# benefit of versioning.
		lowerCAmelCase   =     submodule_path / commit_hash
		lowerCAmelCase   =     full_submodule + os.path.sep + commit_hash
		create_dynamic_module(_SCREAMING_SNAKE_CASE							)

		if not (submodule_path / module_file).exists():
			shutil.copy(_SCREAMING_SNAKE_CASE							,		submodule_path / module_file							)
		# Make sure we also have every file with relative
		for module_needed in modules_needed:
			if not (submodule_path / module_needed).exists():
				get_cached_module_file(
				    _SCREAMING_SNAKE_CASE							,		f'{module_needed}.py'							,		cache_dir=_SCREAMING_SNAKE_CASE							,		force_download=_SCREAMING_SNAKE_CASE							,		resume_download=_SCREAMING_SNAKE_CASE							,		proxies=_SCREAMING_SNAKE_CASE							,		use_auth_token=_SCREAMING_SNAKE_CASE							,		revision=_SCREAMING_SNAKE_CASE							,		local_files_only=_SCREAMING_SNAKE_CASE							,		)
	return os.path.join(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, os.PathLike]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[str] = None							,		_SCREAMING_SNAKE_CASE					:						Optional[Union[str, os.PathLike]] = None							,		_SCREAMING_SNAKE_CASE					:						bool = False							,		_SCREAMING_SNAKE_CASE					:						bool = False							,		_SCREAMING_SNAKE_CASE					:						Optional[Dict[str, str]] = None							,		_SCREAMING_SNAKE_CASE					:						Optional[Union[bool, str]] = None							,		_SCREAMING_SNAKE_CASE					:						Optional[str] = None							,		_SCREAMING_SNAKE_CASE					:						bool = False							,		**_SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		)      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     get_cached_module_file(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		cache_dir=_SCREAMING_SNAKE_CASE							,		force_download=_SCREAMING_SNAKE_CASE							,		resume_download=_SCREAMING_SNAKE_CASE							,		proxies=_SCREAMING_SNAKE_CASE							,		use_auth_token=_SCREAMING_SNAKE_CASE							,		revision=_SCREAMING_SNAKE_CASE							,		local_files_only=_SCREAMING_SNAKE_CASE							,		)
	return get_class_in_module(_SCREAMING_SNAKE_CASE							,		final_module.replace(""".py"""							,		""""""							)							)




'''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)