




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

transformers_logging.set_verbosity_info()
def     A_	(						snake_case_   :				Optional[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     A_	(						snake_case_   :				Union[str, Any]  ,snake_case_   :				Any  ,snake_case_   :				Dict			):







    '''simple docstring'''
    return max(metric_fn(_lowercase  ,_lowercase			) for gt in ground_truths			)
def     A_	(						snake_case_   :				Dict  ,snake_case_   :				Dict  ,snake_case_   :				Optional[Any]			):







    '''simple docstring'''
    UpperCamelCase :      Optional[Any]      				=							[line.strip() for line in open(_lowercase  ,"""r"""			).readlines()]
    UpperCamelCase :      Tuple      				=							[]

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

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

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

    logger.info(f'F1: {fa:.2f}'			)
    logger.info(f'EM: {em:.2f}'			)
def     A_	(						snake_case_   :				Dict  ,snake_case_   :				Any  ,snake_case_   :				Union[str, Any]			):







    '''simple docstring'''
    UpperCamelCase :      List[Any]      				=							args.k
    UpperCamelCase :      Any      				=							[line.strip() for line in open(_lowercase  ,"""r"""			).readlines()]
    UpperCamelCase :      List[Any]      				=							[line.strip() for line in open(_lowercase  ,"""r"""			).readlines()]

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

    UpperCamelCase :      Tuple      				=							1_0_0.0 * em / total
    logger.info(f'Precision@{k}: {em: .2f}'			)
def     A_	(						snake_case_   :				Any  ,snake_case_   :				List[Any]  ,snake_case_   :				Optional[int]			):







    '''simple docstring'''







    def strip_title(snake_case_   :				List[Any]			):
        if title.startswith("""\""""			):
            UpperCamelCase :      Optional[int]      				=							title[1:]
        if title.endswith("""\""""			):
            UpperCamelCase :      Optional[int]      				=							title[:-1]
        return title

    UpperCamelCase :      List[str]      				=							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _lowercase  ,return_tensors="""pt"""  ,padding=_lowercase  ,truncation=_lowercase  ,)["""input_ids"""].to(args.device			)

    UpperCamelCase :      Tuple      				=							rag_model.rag.question_encoder(_lowercase			)
    UpperCamelCase :      int      				=							question_enc_outputs[0]

    UpperCamelCase :      int      				=							rag_model.retriever(
        _lowercase  ,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 :      Any      				=							rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
    UpperCamelCase :      str      				=							[]
    for docs in all_docs:
        UpperCamelCase :      int      				=							[strip_title(_lowercase			) for title in docs["""title"""]]
        provenance_strings.append("""\t""".join(_lowercase			)			)
    return provenance_strings
def     A_	(						snake_case_   :				Optional[Any]  ,snake_case_   :				Any  ,snake_case_   :				Tuple			):







    '''simple docstring'''







    with torch.no_grad():
        UpperCamelCase :      str      				=							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            _lowercase  ,return_tensors="""pt"""  ,padding=_lowercase  ,truncation=_lowercase			)

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

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

        return answers
def     A_	(						):







    '''simple docstring'''
    UpperCamelCase :      Optional[int]      				=							argparse.ArgumentParser()
    parser.add_argument(
        """--model_type"""  ,choices=["""rag_sequence""", """rag_token""", """bart"""]  ,type=_lowercase  ,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=_lowercase  ,choices=["""exact""", """compressed""", """legacy"""]  ,type=_lowercase  ,help="""RAG model retriever type"""  ,)
    parser.add_argument(
        """--index_path"""  ,default=_lowercase  ,type=_lowercase  ,help="""Path to the retrieval index"""  ,)
    parser.add_argument("""--n_docs"""  ,default=5  ,type=_lowercase  ,help="""Number of retrieved docs"""			)
    parser.add_argument(
        """--model_name_or_path"""  ,default=_lowercase  ,type=_lowercase  ,required=_lowercase  ,help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""  ,)
    parser.add_argument(
        """--eval_mode"""  ,choices=["""e2e""", """retrieval"""]  ,default="""e2e"""  ,type=_lowercase  ,help=(
            """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
            """ precision@k."""
        )  ,)
    parser.add_argument("""--k"""  ,default=1  ,type=_lowercase  ,help="""k for the precision@k calculation"""			)
    parser.add_argument(
        """--evaluation_set"""  ,default=_lowercase  ,type=_lowercase  ,required=_lowercase  ,help="""Path to a file containing evaluation samples"""  ,)
    parser.add_argument(
        """--gold_data_path"""  ,default=_lowercase  ,type=_lowercase  ,required=_lowercase  ,help="""Path to a tab-separated file with gold samples"""  ,)
    parser.add_argument(
        """--gold_data_mode"""  ,default="""qa"""  ,type=_lowercase  ,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=_lowercase  ,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=_lowercase  ,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=_lowercase  ,help="""Number of beams to be used when generating answers"""  ,)
    parser.add_argument("""--min_length"""  ,default=1  ,type=_lowercase  ,help="""Min length of the generated answers"""			)
    parser.add_argument("""--max_length"""  ,default=5_0  ,type=_lowercase  ,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 :      Optional[int]      				=							parser.parse_args()
    UpperCamelCase :      Dict      				=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)
    return args
def     A_	(						snake_case_   :				Optional[Any]			):







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

    UpperCamelCase :      Any      				=							(
        [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"""  ,_lowercase			)

    UpperCamelCase :      Tuple      				=							get_scores if args.eval_mode == """e2e""" else get_precision_at_k
    UpperCamelCase :      Dict      				=							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(_lowercase  ,args.predictions_path  ,args.gold_data_path			)
            continue

        logger.info("""***** Running evaluation for {} *****""".format(_lowercase			)			)
        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 :      List[str]      				=							RagRetriever.from_pretrained(_lowercase  ,**_lowercase			)
            UpperCamelCase :      List[str]      				=							model_class.from_pretrained(_lowercase  ,retriever=_lowercase  ,**_lowercase			)
            model.retriever.init_retrieval()
        else:
            UpperCamelCase :      List[str]      				=							model_class.from_pretrained(_lowercase  ,**_lowercase			)
        model.to(args.device			)

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

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


if __name__ == "__main__":
  __A       :						int												=			get_args()
  main(args)












import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							A_       =    "hf-internal-testing/tiny-stable-diffusion-pipe"
							A_       =    1
							A_       =    77
							A_       =    32
							A_       =    4
							A_       =    A_       =    64
							A_       =    torch.manual_seed(0    )

							def 					__A       (     self:      List[Any]						,   __A:      List[Any]=0.9_999      )			-> Dict:
												_A									=       UNetaDConditionModel.from_pretrained(self.model_id						,   subfolder='''unet'''      )
												_A									=       unet.to(__A      )
												_A									=       EMAModel(unet.parameters()						,   decay=__A						,   model_cls=__A						,   model_config=unet.config      )
												return unet, ema_unet

							def 					__A       (     self:      Tuple      )			-> Union[str, Any]:
												_A									=       torch.randn(
												    self.batch_size						,   self.num_in_channels						,   self.latent_height						,   self.latent_width						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randint(0						,   10_00						,   size=(self.batch_size,)						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randn(
												    self.batch_size						,   self.prompt_length						,   self.text_encoder_hidden_dim						,   generator=self.generator      ).to(__A      )
												return noisy_latents, timesteps, encoder_hidden_states

							def 					__A       (     self:      Tuple						,   __A:      Optional[int]      )			-> Optional[Any]:
												_A									=       {}
												for k, param in unet.state_dict().items():
																	_A									=       torch.randn_like(__A      ) + (param * torch.randn_like(__A      ))
																	updated_state_dict.update({k: updated_param}      )
												unet.load_state_dict(__A      )
												return unet

							def 					__A       (     self:      Any      )			-> Any:
												_A							,_A									=       self.get_models()
												# Take the first (hypothetical) EMA step.
												ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 1

												# Take two more.
												for _ in range(2      ):
																	ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 3

							def 					__A       (     self:      Union[str, Any]      )			-> str:
												_A							,_A									=       self.get_models()
												# Since the `unet` is not being updated (i.e., backprop'd)
												# there won't be any difference between the `params` of `unet`
												# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
												ema_unet.step(unet.parameters()      )
												_A									=       list(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

												# The above holds true even if we call `ema.step()` multiple times since
												# `unet` params are still not being updated.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Tuple      )			-> List[str]:
												_A							,_A									=       self.get_models()
												# Here we simulate the parameter updates for `unet`. Since there might
												# be some parameters which are initialized to zero we take extra care to
												# initialize their values to something non-zero before the multiplication.
												_A									=       self.simulate_backprop(__A      )

												# Take the EMA step.
												ema_unet.step(unet_pseudo_updated_step_one.parameters()      )

												# Now the EMA'd parameters won't be equal to the original model parameters.
												_A									=       list(unet_pseudo_updated_step_one.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

												# Ensure this is the case when we take multiple EMA steps.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Optional[int]      )			-> Optional[int]:
												# If we call EMA step after a backpropagation consecutively for two times,
												# the shadow params from those two steps should be different.
												_A							,_A									=       self.get_models()

												# First backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												# Second backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      int      )			-> Any:
												# If there's no decay even if there are backprops, EMA steps
												# won't take any effect i.e., the shadow params would remain the
												# same.
												_A							,_A									=       self.get_models(decay=0.0      )
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert torch.allclose(__A						,   __A      )



							@skip_mps
							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A							,_A									=       self.get_models()
												_A							,_A							,_A									=       self.get_dummy_inputs()

												with tempfile.TemporaryDirectory() as tmpdir:
																	ema_unet.save_pretrained(__A      )
																	_A									=       UNetaDConditionModel.from_pretrained(__A						,   model_cls=__A      )
																	_A									=       loaded_unet.to(unet.device      )

												# Since no EMA step has been performed the outputs should match.
												_A									=       unet(__A						,   __A						,   __A      ).sample
												_A									=       loaded_unet(__A						,   __A						,   __A      ).sample

												assert torch.allclose(__A						,   __A						,   atol=1e-4      )



