


'''simple docstring'''


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							pass







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=False  ):







				"""simple docstring"""





				try:
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   ast.parse(SCREAMING_SNAKE_CASE__  )
				except SyntaxError as e:
								print("""The code generated by the agent is not valid.\n"""							,   SCREAMING_SNAKE_CASE__  )
								return
				if state is None:
								_SCREAMING_SNAKE_CASE				: str  	=   {}
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   None
				for idx, node in enumerate(expression.body  ):
								try:
												_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								except InterpretorError as e:
												_SCREAMING_SNAKE_CASE				: str  	=   f"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
												if chat_mode:
																msg += (
																    f""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
																)
												else:
																msg += f""":\n{e}"""
												print(SCREAMING_SNAKE_CASE__  )
												break
								if line_result is not None:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   line_result

				return result







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				if isinstance(SCREAMING_SNAKE_CASE__							,   ast.Assign  ):
								# Assignement -> we evaluate the assignement which should update the state
								# We return the variable assigned as it may be used to determine the final result.
								return evaluate_assign(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Call  ):
								# Function call -> we return the value of the function call
								return evaluate_call(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Constant  ):
								# Constant -> just return the value
								return expression.value
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Dict  ):
								# Dict -> evaluate all keys and values
								_SCREAMING_SNAKE_CASE				: Tuple  	=   [evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for k in expression.keys]
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   [evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for v in expression.values]
								return dict(zip(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Expr  ):
								# Expression -> evaluate the content
								return evaluate_ast(expression.value							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.For  ):
								# For loop -> execute the loop
								return evaluate_for(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.FormattedValue  ):
								# Formatted value (part of f-string) -> evaluate the content and return
								return evaluate_ast(expression.value							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.If  ):
								# If -> execute the right branch
								return evaluate_if(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif hasattr(SCREAMING_SNAKE_CASE__							,   """Index"""  ) and isinstance(SCREAMING_SNAKE_CASE__							,   ast.Index  ):
								return evaluate_ast(expression.value							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.JoinedStr  ):
								return "".join([str(evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )  ) for v in expression.values]  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.List  ):
								# List -> evaluate all elements
								return [evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for elt in expression.elts]
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Name  ):
								# Name -> pick up the value in the state
								return evaluate_name(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Subscript  ):
								# Subscript -> return the value of the indexing
								return evaluate_subscript(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				else:
								# For now we refuse anything else. Let's add things as we need them.
								raise InterpretorError(f"""{expression.__class__.__name__} is not supported."""  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   assign.targets
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   evaluate_ast(assign.value							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

				if len(SCREAMING_SNAKE_CASE__  ) == 1:
								_SCREAMING_SNAKE_CASE				: List[str]  	=   result
				else:
								if len(SCREAMING_SNAKE_CASE__  ) != len(SCREAMING_SNAKE_CASE__  ):
												raise InterpretorError(f"""Expected {len(SCREAMING_SNAKE_CASE__  )} values but got {len(SCREAMING_SNAKE_CASE__  )}."""  )
								for var_name, r in zip(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
												_SCREAMING_SNAKE_CASE				: int  	=   r
				return result







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				if not isinstance(call.func							,   ast.Name  ):
								raise InterpretorError(
								    f"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
								    f"""type {type(call.func  )}."""  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   call.func.id
				if func_name not in tools:
								raise InterpretorError(
								    f"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""  )

				_SCREAMING_SNAKE_CASE				: int  	=   tools[func_name]
				# Todo deal with args
				_SCREAMING_SNAKE_CASE				: List[str]  	=   [evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for arg in call.args]
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   {keyword.arg: evaluate_ast(keyword.value							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for keyword in call.keywords}
				return func(*SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: int  	=   evaluate_ast(subscript.slice							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   evaluate_ast(subscript.value							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				if isinstance(SCREAMING_SNAKE_CASE__							,   (list, tuple)  ):
								return value[int(SCREAMING_SNAKE_CASE__  )]
				if index in value:
								return value[index]
				if isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) and isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   difflib.get_close_matches(SCREAMING_SNAKE_CASE__							,   list(value.keys()  )  )
								if len(SCREAMING_SNAKE_CASE__  ) > 0:
												return value[close_matches[0]]

				raise InterpretorError(f"""Could not index {value} with '{index}'."""  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				if name.id in state:
								return state[name.id]
				_SCREAMING_SNAKE_CASE				: str  	=   difflib.get_close_matches(name.id							,   list(state.keys()  )  )
				if len(SCREAMING_SNAKE_CASE__  ) > 0:
								return state[close_matches[0]]
				raise InterpretorError(f"""The variable `{name.id}` is not defined."""  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				if len(condition.ops  ) > 1:
								raise InterpretorError("""Cannot evaluate conditions with multiple operators"""  )

				_SCREAMING_SNAKE_CASE				: Tuple  	=   evaluate_ast(condition.left							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   condition.ops[0]
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   evaluate_ast(condition.comparators[0]							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

				if isinstance(SCREAMING_SNAKE_CASE__							,   ast.Eq  ):
								return left == right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.NotEq  ):
								return left != right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Lt  ):
								return left < right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.LtE  ):
								return left <= right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Gt  ):
								return left > right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.GtE  ):
								return left >= right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.Is  ):
								return left is right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.IsNot  ):
								return left is not right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.In  ):
								return left in right
				elif isinstance(SCREAMING_SNAKE_CASE__							,   ast.NotIn  ):
								return left not in right
				else:
								raise InterpretorError(f"""Operator not supported: {comparator}"""  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   None
				if evaluate_condition(if_statement.test							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
								for line in if_statement.body:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												if line_result is not None:
																_SCREAMING_SNAKE_CASE				: Tuple  	=   line_result
				else:
								for line in if_statement.orelse:
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												if line_result is not None:
																_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   line_result
				return result







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[Any]  	=   None
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   evaluate_ast(for_loop.iter							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				for counter in iterator:
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   counter
								for expression in for_loop.body:
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   evaluate_ast(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												if line_result is not None:
																_SCREAMING_SNAKE_CASE				: int  	=   line_result
				return result








'''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_	:     str							     =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""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__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   [line.strip() for line in open(SCREAMING_SNAKE_CASE__							,   """r"""  ).readlines()]
				_SCREAMING_SNAKE_CASE				: str  	=   []

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

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   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__  )

				_SCREAMING_SNAKE_CASE				: str  	=   1_0_0.0 * em / total
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   1_0_0.0 * fa / total

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args.k
				_SCREAMING_SNAKE_CASE				: str  	=   [line.strip() for line in open(SCREAMING_SNAKE_CASE__							,   """r"""  ).readlines()]
				_SCREAMING_SNAKE_CASE				: str  	=   [line.strip() for line in open(SCREAMING_SNAKE_CASE__							,   """r"""  ).readlines()]

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

				_SCREAMING_SNAKE_CASE				: str  	=   1_0_0.0 * em / total
				logger.info(f"""Precision@{k}: {em: .2f}"""  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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

				_SCREAMING_SNAKE_CASE				: Dict  	=   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  )

				_SCREAMING_SNAKE_CASE				: List[str]  	=   rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   question_enc_outputs[0]

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   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"""							,   )
				_SCREAMING_SNAKE_CASE				: int  	=   rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
				_SCREAMING_SNAKE_CASE				: int  	=   []
				for docs in all_docs:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   [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__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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

								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   inputs_dict.input_ids.to(args.device  )
								_SCREAMING_SNAKE_CASE				: Dict  	=   inputs_dict.attention_mask.to(args.device  )
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   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]]							,   )
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   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_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   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."""							,   )
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   parser.parse_args()
				_SCREAMING_SNAKE_CASE				: int  	=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
				return args







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				if args.model_type is None:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   infer_model_type(args.model_name_or_path  )
								assert args.model_type is not None
				if args.model_type.startswith("""rag"""  ):
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
								_SCREAMING_SNAKE_CASE				: str  	=   args.n_docs
								if args.index_name is not None:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args.index_name
								if args.index_path is not None:
												_SCREAMING_SNAKE_CASE				: str  	=   args.index_path
				else:
								_SCREAMING_SNAKE_CASE				: Any  	=   BartForConditionalGeneration

				_SCREAMING_SNAKE_CASE				: 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"""							,   SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Dict  	=   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(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"""  ):
												_SCREAMING_SNAKE_CASE				: Any  	=   RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: int  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__							,   retriever=SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
												model.retriever.init_retrieval()
								else:
												_SCREAMING_SNAKE_CASE				: Dict  	=   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:
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   []
												for line in tqdm(SCREAMING_SNAKE_CASE__  ):
																questions.append(line.strip()  )
																if len(SCREAMING_SNAKE_CASE__  ) == args.eval_batch_size:
																				_SCREAMING_SNAKE_CASE				: Any  	=   evaluate_batch_fn(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
																				preds_file.write("""\n""".join(SCREAMING_SNAKE_CASE__  ) + """\n"""  )
																				preds_file.flush()
																				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   []
												if len(SCREAMING_SNAKE_CASE__  ) > 0:
																_SCREAMING_SNAKE_CASE				: str  	=   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_	:     int							     =	get_args()
				main(args)




