



'''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


A_				:					Dict							=      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def        snake_case_   (	lowerCAmelCase_						)->						Union[str, 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_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]
       _UpperCAmelCase    :		List[str]     	=					[]

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

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

       _UpperCAmelCase    :		List[Any]     	=					1_0_0.0 * em / total
       _UpperCAmelCase    :		Dict     	=					1_0_0.0 * fa / total

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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					args.k
       _UpperCAmelCase    :		Tuple     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]
       _UpperCAmelCase    :		List[str]     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]

       _UpperCAmelCase    :		List[Any]     	=					0
       for hypo, reference in zip(lowerCAmelCase_			,  lowerCAmelCase_						):
              _UpperCAmelCase    :		List[Any]     	=					set(hypo.split("""\t"""						)[:k]						)
              _UpperCAmelCase    :		Optional[int]     	=					set(reference.split("""\t"""						)						)
              total += 1
              em += len(hypo_provenance & ref_provenance						) / k

       _UpperCAmelCase    :		Optional[Any]     	=					1_0_0.0 * em / total
       logger.info(F'''Precision@{k}: {em: .2f}'''						)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       def strip_title(lowerCAmelCase_						):
              if title.startswith("""\""""						):
                     _UpperCAmelCase    :		str     	=					title[1:]
              if title.endswith("""\""""						):
                     _UpperCAmelCase    :		Tuple     	=					title[:-1]
              return title

       _UpperCAmelCase    :		List[Any]     	=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           lowerCAmelCase_			,  return_tensors="""pt"""			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_			,  )["""input_ids"""].to(args.device						)

       _UpperCAmelCase    :		Any     	=					rag_model.rag.question_encoder(lowerCAmelCase_						)
       _UpperCAmelCase    :		Optional[int]     	=					question_enc_outputs[0]

       _UpperCAmelCase    :		Optional[int]     	=					rag_model.retriever(
           lowerCAmelCase_			,  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"""			,  )
       _UpperCAmelCase    :		List[str]     	=					rag_model.retriever.index.get_doc_dicts(result.doc_ids						)
       _UpperCAmelCase    :		List[str]     	=					[]
       for docs in all_docs:
              _UpperCAmelCase    :		Union[str, Any]     	=					[strip_title(lowerCAmelCase_						) for title in docs["""title"""]]
              provenance_strings.append("""\t""".join(lowerCAmelCase_						)						)
       return provenance_strings




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       with torch.no_grad():
              _UpperCAmelCase    :		List[Any]     	=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  lowerCAmelCase_			,  return_tensors="""pt"""			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_						)

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

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

              return answers




def        snake_case_   (	)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					argparse.ArgumentParser()
       parser.add_argument(
           """--model_type"""			,  choices=["""rag_sequence""", """rag_token""", """bart"""]			,  type=lowerCAmelCase_			,  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=lowerCAmelCase_			,  choices=["""exact""", """compressed""", """legacy"""]			,  type=lowerCAmelCase_			,  help="""RAG model retriever type"""			,  )
       parser.add_argument(
           """--index_path"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  help="""Path to the retrieval index"""			,  )
       parser.add_argument("""--n_docs"""			,  default=5			,  type=lowerCAmelCase_			,  help="""Number of retrieved docs"""						)
       parser.add_argument(
           """--model_name_or_path"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  required=lowerCAmelCase_			,  help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""			,  )
       parser.add_argument(
           """--eval_mode"""			,  choices=["""e2e""", """retrieval"""]			,  default="""e2e"""			,  type=lowerCAmelCase_			,  help=(
               """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
               """ precision@k."""
           )			,  )
       parser.add_argument("""--k"""			,  default=1			,  type=lowerCAmelCase_			,  help="""k for the precision@k calculation"""						)
       parser.add_argument(
           """--evaluation_set"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  required=lowerCAmelCase_			,  help="""Path to a file containing evaluation samples"""			,  )
       parser.add_argument(
           """--gold_data_path"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  required=lowerCAmelCase_			,  help="""Path to a tab-separated file with gold samples"""			,  )
       parser.add_argument(
           """--gold_data_mode"""			,  default="""qa"""			,  type=lowerCAmelCase_			,  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=lowerCAmelCase_			,  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=lowerCAmelCase_			,  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=lowerCAmelCase_			,  help="""Number of beams to be used when generating answers"""			,  )
       parser.add_argument("""--min_length"""			,  default=1			,  type=lowerCAmelCase_			,  help="""Min length of the generated answers"""						)
       parser.add_argument("""--max_length"""			,  default=50			,  type=lowerCAmelCase_			,  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."""			,  )
       _UpperCAmelCase    :		Any     	=					parser.parse_args()
       _UpperCAmelCase    :		Dict     	=					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)
       return args




def        snake_case_   (	lowerCAmelCase_						)->						Dict:





       '''simple docstring'''


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

       _UpperCAmelCase    :		Optional[int]     	=					(
           [f.path for f in os.scandir(args.model_name_or_path						) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info("""Evaluate the following checkpoints: %s"""			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		str     	=					get_scores if args.eval_mode == """e2e""" else get_precision_at_k
       _UpperCAmelCase    :		Union[str, Any]     	=					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(lowerCAmelCase_			,  args.predictions_path			,  args.gold_data_path						)
                     continue

              logger.info("""***** Running evaluation for {} *****""".format(lowerCAmelCase_						)						)
              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"""						):
                     _UpperCAmelCase    :		Optional[int]     	=					RagRetriever.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_						)
                     _UpperCAmelCase    :		int     	=					model_class.from_pretrained(lowerCAmelCase_			,  retriever=lowerCAmelCase_			,  **lowerCAmelCase_						)
                     model.retriever.init_retrieval()
              else:
                     _UpperCAmelCase    :		Optional[Any]     	=					model_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_						)
              model.to(args.device						)

              with open(args.evaluation_set			,  """r"""						) as eval_file, open(args.predictions_path			,  """w"""						) as preds_file:
                     _UpperCAmelCase    :		List[str]     	=					[]
                     for line in tqdm(lowerCAmelCase_						):
                            questions.append(line.strip()						)
                            if len(lowerCAmelCase_						) == args.eval_batch_size:
                                   _UpperCAmelCase    :		Any     	=					evaluate_batch_fn(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
                                   preds_file.write("""\n""".join(lowerCAmelCase_						) + """\n"""						)
                                   preds_file.flush()
                                   _UpperCAmelCase    :		Optional[int]     	=					[]
                     if len(lowerCAmelCase_						) > 0:
                            _UpperCAmelCase    :		str     	=					evaluate_batch_fn(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
                            preds_file.write("""\n""".join(lowerCAmelCase_						)						)
                            preds_file.flush()

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


if __name__ == "__main__":
   A_				:					Optional[Any]							=      get_args()
   main(args)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : CommonSchedulerState

      # setable values
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : Optional[int]  			=   None

      # running values
      lowerCamelCase_      : Optional[jnp.ndarray]  			=   None







      @classmethod
      def       lowerCAmelCase_       (	cls				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        return cls(common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase  )







@dataclass
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : LMSDiscreteSchedulerState







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : str  			=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase_      : jnp.dtype







      @property
      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return True







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = jnp.floataa				,    )		->			Optional[Any]:
        snake_case_					= dtype







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = None  )		->			LMSDiscreteSchedulerState:
        if common is None:
          snake_case_					= CommonSchedulerState.create(self  )

        snake_case_					= jnp.arange(0				,    self.config.num_train_timesteps  ).round()[::-1]
        snake_case_					= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_					= sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			jnp.ndarray:
        ((snake_case_)		,							)					= jnp.where(state.timesteps == timestep				,    size=1  )
        snake_case_					= step_index[0]

        snake_case_					= state.sigmas[step_index]
        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 1.0
          for k in range(lowerCamelCase  ):
            if current_order == k:
              continue
            prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
          return prod

        snake_case_					= integrate.quad(lowerCamelCase				,    state.sigmas[t]				,    state.sigmas[t + 1]				,    epsrel=1e-4  )[0]

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = ()  )		->			LMSDiscreteSchedulerState:
        snake_case_					= jnp.linspace(self.config.num_train_timesteps - 1				,    0				,    lowerCamelCase				,    dtype=self.dtype  )

        snake_case_					= jnp.floor(lowerCamelCase  ).astype(jnp.intaa  )
        snake_case_					= jnp.ceil(lowerCamelCase  ).astype(jnp.intaa  )

        snake_case_					= jnp.mod(lowerCamelCase				,    1.0  )

        snake_case_					= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_					= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_					= jnp.concatenate([sigmas, jnp.array([0.0]				,    dtype=self.dtype  )]  )

        snake_case_					= timesteps.astype(jnp.intaa  )

        # initial running values
        snake_case_					= jnp.zeros((0,) + shape				,    dtype=self.dtype  )

        return state.replace(
            timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    num_inference_steps=lowerCamelCase				,    derivatives=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    )		->			Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

        snake_case_					= state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma
        snake_case_					= state.replace(derivatives=jnp.append(state.derivatives				,    lowerCamelCase  )  )
        if len(state.derivatives  ) > order:
          snake_case_					= state.replace(derivatives=jnp.delete(state.derivatives				,    0  )  )

        # 3. Compute linear multistep coefficients
        snake_case_					= min(timestep + 1				,    lowerCamelCase  )
        snake_case_					= [self.get_lms_coefficient(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  ) for curr_order in range(lowerCamelCase  )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCamelCase				,    reversed(state.derivatives  )  )  )

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,    state=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			jnp.ndarray:
        snake_case_					= state.sigmas[timesteps].flatten()
        snake_case_					= broadcast_to_shape_from_left(lowerCamelCase				,    noise.shape  )

        snake_case_					= original_samples + noise * sigma

        return noisy_samples







      def __len__(	self  )		->			Optional[Any]:
        return self.config.num_train_timesteps