
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_				:							List[str]       =    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		List[str]					)       ->   str:
       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        __lowercase(							__snake_case				:		Any    ,__snake_case				:		int    ,__snake_case				:		Any					)       ->   Optional[Any]:
       return max(metric_fn(__snake_case    ,__snake_case					) for gt in ground_truths					)
def        __lowercase(							__snake_case				:		Tuple    ,__snake_case				:		Tuple    ,__snake_case				:		Any					)       ->   Union[str, Any]:
       __snake_case						       =   [line.strip() for line in open(__snake_case    ,'r'					).readlines()]
       __snake_case						       =   []

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

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

       __snake_case						       =   1_00.0 * em / total
       __snake_case						       =   1_00.0 * fa / total

       logger.info(f'''F1: {fa:.2f}'''					)
       logger.info(f'''EM: {em:.2f}'''					)
def        __lowercase(							__snake_case				:		Tuple    ,__snake_case				:		Tuple    ,__snake_case				:		Optional[Any]					)       ->   Optional[Any]:
       __snake_case						       =   args.k
       __snake_case						       =   [line.strip() for line in open(__snake_case    ,'r'					).readlines()]
       __snake_case						       =   [line.strip() for line in open(__snake_case    ,'r'					).readlines()]

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

       __snake_case						       =   1_00.0 * em / total
       logger.info(f'''Precision@{k}: {em: .2f}'''					)
def        __lowercase(							__snake_case				:		Any    ,__snake_case				:		Any    ,__snake_case				:		int					)       ->   Dict:
       def strip_title(__snake_case				:		Dict					):
              if title.startswith('"'					):
                     __snake_case						       =   title[1:]
              if title.endswith('"'					):
                     __snake_case						       =   title[:-1]
              return title

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

       __snake_case						       =   rag_model.rag.question_encoder(__snake_case					)
       __snake_case						       =   question_enc_outputs[0]

       __snake_case						       =   rag_model.retriever(
           __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'    ,)
       __snake_case						       =   rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       __snake_case						       =   []
       for docs in all_docs:
              __snake_case						       =   [strip_title(__snake_case					) for title in docs['title']]
              provenance_strings.append('\t'.join(__snake_case					)					)
       return provenance_strings
def        __lowercase(							__snake_case				:		Tuple    ,__snake_case				:		str    ,__snake_case				:		Dict					)       ->   int:
       with torch.no_grad():
              __snake_case						       =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  __snake_case    ,return_tensors='pt'    ,padding=__snake_case    ,truncation=__snake_case					)

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

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

              return answers
def        __lowercase(							)       ->   str:
       __snake_case						       =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type'    ,choices=['rag_sequence', 'rag_token', 'bart']    ,type=__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=__snake_case    ,choices=['exact', 'compressed', 'legacy']    ,type=__snake_case    ,help='RAG model retriever type'    ,)
       parser.add_argument(
           '--index_path'    ,default=__snake_case    ,type=__snake_case    ,help='Path to the retrieval index'    ,)
       parser.add_argument('--n_docs'    ,default=5    ,type=__snake_case    ,help='Number of retrieved docs'					)
       parser.add_argument(
           '--model_name_or_path'    ,default=__snake_case    ,type=__snake_case    ,required=__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=__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=__snake_case    ,help='k for the precision@k calculation'					)
       parser.add_argument(
           '--evaluation_set'    ,default=__snake_case    ,type=__snake_case    ,required=__snake_case    ,help='Path to a file containing evaluation samples'    ,)
       parser.add_argument(
           '--gold_data_path'    ,default=__snake_case    ,type=__snake_case    ,required=__snake_case    ,help='Path to a tab-separated file with gold samples'    ,)
       parser.add_argument(
           '--gold_data_mode'    ,default='qa'    ,type=__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=__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=__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=__snake_case    ,help='Number of beams to be used when generating answers'    ,)
       parser.add_argument('--min_length'    ,default=1    ,type=__snake_case    ,help='Min length of the generated answers'					)
       parser.add_argument('--max_length'    ,default=50    ,type=__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.'    ,)
       __snake_case						       =   parser.parse_args()
       __snake_case						       =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
       return args
def        __lowercase(							__snake_case				:		Union[str, Any]					)       ->   List[str]:
       __snake_case						       =   {}
       if args.model_type is None:
              __snake_case						       =   infer_model_type(args.model_name_or_path					)
              assert args.model_type is not None
       if args.model_type.startswith('rag'					):
              __snake_case						       =   RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
              __snake_case						       =   args.n_docs
              if args.index_name is not None:
                     __snake_case						       =   args.index_name
              if args.index_path is not None:
                     __snake_case						       =   args.index_path
       else:
              __snake_case						       =   BartForConditionalGeneration

       __snake_case						       =   (
           [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'    ,__snake_case					)

       __snake_case						       =   get_scores if args.eval_mode == 'e2e' else get_precision_at_k
       __snake_case						       =   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(__snake_case    ,args.predictions_path    ,args.gold_data_path					)
                     continue

              logger.info('***** Running evaluation for {} *****'.format(__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'					):
                     __snake_case						       =   RagRetriever.from_pretrained(__snake_case    ,**__snake_case					)
                     __snake_case						       =   model_class.from_pretrained(__snake_case    ,retriever=__snake_case    ,**__snake_case					)
                     model.retriever.init_retrieval()
              else:
                     __snake_case						       =   model_class.from_pretrained(__snake_case    ,**__snake_case					)
              model.to(args.device					)

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

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


if __name__ == "__main__":
 lowerCamelCase_				:							Optional[int]       =    get_args()
 main(args)


import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 lowerCamelCase_				:							Union[str, Any]       =    object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   value

     def __str__(  self    ):
            return self.to_string()

     def 						__lowerCamelCase				(  self    ):
            logger.error(
                'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'    )
            return self._value

     def 						__lowerCamelCase				(  self    ):
            logger.error(
                'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'    )
            return str(self._value    )







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def 						__lowerCamelCase				(  self    ):
            return self._value

     def 						__lowerCamelCase				(  self    ):
            return self._value







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

            if not is_vision_available():
                   raise ImportError('PIL must be installed in order to handle images.'    )

            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   None

            if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       (str, pathlib.Path)    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                   __snake_case						       =   value
            else:
                   raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_    )}'''    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Image, display

            display(Image(self.to_string()    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._raw is not None:
                   return self._raw

            if self._path is not None:
                   __snake_case						       =   Image.open(self._path    )
                   return self._raw

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

            if self._raw is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.png'    )
                   self._raw.save(self._path    )

                   return self._path

            if self._tensor is not None:
                   __snake_case						       =   self._tensor.cpu().detach().numpy()

                   # There is likely simpler than load into image into save
                   __snake_case						       =   Image.fromarray((array * 255).astype(np.uinta    )    )

                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.png'    )

                   img.save(self._path    )

                   return self._path







class  _lowerCamelCase			(lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=16_000    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

            if not is_soundfile_availble():
                   raise ImportError('soundfile must be installed in order to handle audio.'    )

            __snake_case						       =   None
            __snake_case						       =   None

            __snake_case						       =   samplerate

            if isinstance(SCREAMING_SNAKE_CASE_							,       (str, pathlib.Path)    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                   __snake_case						       =   value
            else:
                   raise ValueError(f'''Unsupported audio type: {type(SCREAMING_SNAKE_CASE_    )}'''    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Audio, display

            display(Audio(self.to_string()							,       rate=self.samplerate    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._tensor is not None:
                   return self._tensor

            if self._path is not None:
                   __snake_case       ,       __snake_case						       =   sf.read(self._path    )
                   __snake_case						       =   torch.tensor(SCREAMING_SNAKE_CASE_    )
                   return self._tensor

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

            if self._tensor is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.wav'    )
                   sf.write(self._path							,       self._tensor							,       samplerate=self.samplerate    )
                   return self._path





lowerCamelCase_				:							Optional[Any]       =    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowerCamelCase_				:							Any       =    {str: AgentText}

if is_vision_available():
 lowerCamelCase_				:							Optional[int]       =    AgentImage
def        __lowercase(							*__snake_case				:		Optional[Any]    ,**__snake_case				:		str					)       ->   Tuple:
       __snake_case						       =   [(arg.to_raw() if isinstance(__snake_case    ,__snake_case					) else arg) for arg in args]
       __snake_case						       =   {k: (v.to_raw() if isinstance(__snake_case    ,__snake_case					) else v) for k, v in kwargs.items()}
       return args, kwargs
def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		List[str]=None					)       ->   Tuple:
       if isinstance(__snake_case    ,__snake_case					):
              __snake_case						       =   {}
              for i, (k, v) in enumerate(outputs.items()					):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   __snake_case						       =   AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)
                            else:
                                   __snake_case						       =   AgentType(__snake_case					)

                     else:
                            # If the class does not have defined output, then we map according to the type
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          __snake_case						       =   _v(__snake_case					)
                            if k not in decoded_outputs:
                                   __snake_case						       =   AgentType[v]

       elif isinstance(__snake_case    ,(list, tuple)					):
              __snake_case						       =   type(__snake_case					)()
              for i, v in enumerate(__snake_case					):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)					)
                            else:
                                   decoded_outputs.append(AgentType(__snake_case					)					)
                     else:
                            # If the class does not have defined output, then we map according to the type
                            __snake_case						       =   False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          decoded_outputs.append(_v(__snake_case					)					)
                                          __snake_case						       =   True

                            if not found:
                                   decoded_outputs.append(AgentType(__snake_case					)					)

       else:
              if output_types[0] in AGENT_TYPE_MAPPING:
                     # If the class has defined outputs, we can map directly according to the class definition
                     __snake_case						       =   AGENT_TYPE_MAPPING[output_types[0]](__snake_case					)

              else:
                     # If the class does not have defined output, then we map according to the type
                     for _k, _v in INSTANCE_TYPE_MAPPING.items():
                            if isinstance(__snake_case    ,_k					):
                                   return _v(__snake_case					)
                     return AgentType(__snake_case					)

       return decoded_outputs
