'''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


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

transformers_logging.set_verbosity_info()





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      List[Any]:
    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      _UpperCAmelCase    (							_UpperCamelCase    :	List[Any],   _UpperCamelCase    :	Any,   _UpperCamelCase    :	str   )	->      Optional[Any]:
    return max(metric_fn(_UpperCamelCase,   _UpperCamelCase   ) for gt in ground_truths   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Any,   _UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    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_     =   1_0_0.0 * em / total
    A_     =   1_0_0.0 * fa / total

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





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	str   )	->      Dict:
    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_     =   1_0_0.0 * em / total
    logger.info(F'''Precision@{k}: {em: .2f}'''   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    def strip_title(_UpperCamelCase    :	List[Any]   ):
        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      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	List[str]   )	->      List[str]:
    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      _UpperCAmelCase    (							)	->      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      _UpperCAmelCase    (							_UpperCamelCase    :	Any   )	->      Tuple:
    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__":
  __snake_case   :      List[Any]						    =  get_args()
  main(args)







'''simple docstring'''




# Copyright 2021 The HuggingFace 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 json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__snake_case   :      Union[str, Any]						    =  os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__snake_case   :      Union[str, Any]						    =  os.path.join(hf_cache_home, 'accelerate')
__snake_case   :      Any						    =  os.path.join(cache_dir, 'default_config.yaml')
__snake_case   :      Optional[Any]						    =  os.path.join(cache_dir, 'default_config.yaml')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
  __snake_case   :      List[Any]						    =  default_yaml_config_file
else:
  __snake_case   :      Any						    =  default_json_config_file





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[Any]   )	->      Any:
    if config_file is not None:
        if not os.path.isfile(_UpperCamelCase   ):
            raise FileNotFoundError(
                F'''The passed configuration file `{config_file}` does not exist. '''
                '''Please pass an existing file to `accelerate launch`, or use the the default one '''
                '''created through `accelerate config` and run `accelerate launch` '''
                '''without the `--config_file` argument.'''   )
    else:
        A_     =   default_config_file
    with open(_UpperCamelCase,   '''r''',   encoding='''utf-8'''   ) as f:
        if config_file.endswith('''.json'''   ):
            if (
                json.load(_UpperCamelCase   ).get('''compute_environment''',   ComputeEnvironment.LOCAL_MACHINE   )
                == ComputeEnvironment.LOCAL_MACHINE
            ):
                A_     =   ClusterConfig
            else:
                A_     =   SageMakerConfig
            return config_class.from_json_file(json_file=_UpperCamelCase   )
        else:
            if (
                yaml.safe_load(_UpperCamelCase   ).get('''compute_environment''',   ComputeEnvironment.LOCAL_MACHINE   )
                == ComputeEnvironment.LOCAL_MACHINE
            ):
                A_     =   ClusterConfig
            else:
                A_     =   SageMakerConfig
            return config_class.from_yaml_file(yaml_file=_UpperCamelCase   )




@dataclass
class 	__UpperCAmelCase				:





       '''simple docstring'''



       __lowercase					:					ComputeEnvironment
       __lowercase					:					Union[DistributedType, SageMakerDistributedType]
       __lowercase					:					str
       __lowercase					:					bool

       def 					__A       (      self )		->     Any:
           A_     =   self.__dict__
           # For serialization, it's best to convert Enums to strings (or their underlying value type).
           for key, value in result.items():
               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   A_     =   value.value
               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) and not bool(_SCREAMING_SNAKE_CASE ):
                   A_     =   None
           A_     =   {k: v for k, v in result.items() if v is not None}
           return result

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE=None )		->     Union[str, Any]:
           A_     =   default_json_config_file if json_file is None else json_file
           with open(_SCREAMING_SNAKE_CASE    ,     '''r'''    ,     encoding='''utf-8''' ) as f:
               A_     =   json.load(_SCREAMING_SNAKE_CASE )
           if "compute_environment" not in config_dict:
               A_     =   ComputeEnvironment.LOCAL_MACHINE
           if "mixed_precision" not in config_dict:
               A_     =   '''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
           if "fp16" in config_dict:  # Convert the config to the new format.
               del config_dict["fp16"]
           if "dynamo_backend" in config_dict:  # Convert the config to the new format.
               A_     =   config_dict.pop('''dynamo_backend''' )
               A_     =   {} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
           if "use_cpu" not in config_dict:
               A_     =   False
           return cls(**_SCREAMING_SNAKE_CASE )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Union[str, Any]:
           with open(_SCREAMING_SNAKE_CASE    ,     '''w'''    ,     encoding='''utf-8''' ) as f:
               A_     =   json.dumps(self.to_dict()    ,     indent=2    ,     sort_keys=_SCREAMING_SNAKE_CASE ) + '''\n'''
               f.write(_SCREAMING_SNAKE_CASE )

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE=None )		->     Optional[int]:
           A_     =   default_yaml_config_file if yaml_file is None else yaml_file
           with open(_SCREAMING_SNAKE_CASE    ,     '''r'''    ,     encoding='''utf-8''' ) as f:
               A_     =   yaml.safe_load(_SCREAMING_SNAKE_CASE )
           if "compute_environment" not in config_dict:
               A_     =   ComputeEnvironment.LOCAL_MACHINE

           if "mixed_precision" not in config_dict:
               A_     =   '''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
           if isinstance(config_dict['''mixed_precision''']    ,     _SCREAMING_SNAKE_CASE ) and not config_dict["mixed_precision"]:
               A_     =   '''no'''
           if "fp16" in config_dict:  # Convert the config to the new format.
               del config_dict["fp16"]
           if "dynamo_backend" in config_dict:  # Convert the config to the new format.
               A_     =   config_dict.pop('''dynamo_backend''' )
               A_     =   {} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
           if "use_cpu" not in config_dict:
               A_     =   False
           return cls(**_SCREAMING_SNAKE_CASE )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     int:
           with open(_SCREAMING_SNAKE_CASE    ,     '''w'''    ,     encoding='''utf-8''' ) as f:
               yaml.safe_dump(self.to_dict()    ,     _SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Tuple:
           if isinstance(self.compute_environment    ,     _SCREAMING_SNAKE_CASE ):
               A_     =   ComputeEnvironment(self.compute_environment )
           if isinstance(self.distributed_type    ,     _SCREAMING_SNAKE_CASE ):
               if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                   A_     =   SageMakerDistributedType(self.distributed_type )
               else:
                   A_     =   DistributedType(self.distributed_type )
           if self.dynamo_config is None:
               A_     =   {}




@dataclass
class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					int
       __lowercase					:					int    =				0
       __lowercase					:					int    =				1
       __lowercase					:					Optional[str]    =				None
       __lowercase					:					Optional[str]    =				None
       __lowercase					:					Optional[int]    =				None
       __lowercase					:					Optional[str]    =				"static"
       __lowercase					:					Optional[bool]    =				False
       __lowercase					:					str    =				"main"

       # args for deepspeed_plugin
       __lowercase					:					dict    =				None
       # args for fsdp
       __lowercase					:					dict    =				None
       # args for megatron_lm
       __lowercase					:					dict    =				None
       # args for ipex
       __lowercase					:					dict    =				None
       # args for TPU
       __lowercase					:					bool    =				False

       # args for TPU pods
       __lowercase					:					str    =				None
       __lowercase					:					str    =				None
       __lowercase					:					bool    =				False
       __lowercase					:					bool    =				False
       __lowercase					:					str    =				None
       __lowercase					:					List[str]    =				None
       __lowercase					:					List[str]    =				None
       __lowercase					:					List[str]    =				None

       # args for dynamo
       __lowercase					:					dict    =				None

       def 					__A       (      self )		->     Any:
           if self.deepspeed_config is None:
               A_     =   {}
           if self.fsdp_config is None:
               A_     =   {}
           if self.megatron_lm_config is None:
               A_     =   {}
           if self.ipex_config is None:
               A_     =   {}
           return super().__post_init__()




@dataclass
class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str
       __lowercase					:					str
       __lowercase					:					Optional[str]    =				None
       __lowercase					:					Optional[str]    =				None
       __lowercase					:					str    =				"us-east-1"
       __lowercase					:					int    =				1
       __lowercase					:					str    =				"all"
       __lowercase					:					str    =				f"accelerate-sagemaker-{num_machines}"
       __lowercase					:					str    =				SAGEMAKER_PYTORCH_VERSION
       __lowercase					:					str    =				SAGEMAKER_TRANSFORMERS_VERSION
       __lowercase					:					str    =				SAGEMAKER_PYTHON_VERSION
       __lowercase					:					str    =				None
       __lowercase					:					str    =				None
       __lowercase					:					dict    =				None
       __lowercase					:					dict    =				None






