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

transformers_logging.set_verbosity_info()



def 						__snake_case (       lowerCAmelCase       :     Union[str, 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 						__snake_case (       lowerCAmelCase       :     Any      ,  lowerCAmelCase       :     List[Any]      ,  lowerCAmelCase       :     Union[str, Any]			):
	return max(metric_fn(lowerCAmelCase__      ,  lowerCAmelCase__			) for gt in ground_truths			)



def 						__snake_case (       lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     Tuple			):
	__UpperCAmelCase         =					[line.strip() for line in open(lowerCAmelCase__      ,  'r'			).readlines()]
	__UpperCAmelCase         =					[]

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

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

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



def 						__snake_case (       lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     Tuple      ,  lowerCAmelCase       :     str			):
	__UpperCAmelCase         =					args.k
	__UpperCAmelCase         =					[line.strip() for line in open(lowerCAmelCase__      ,  'r'			).readlines()]
	__UpperCAmelCase         =					[line.strip() for line in open(lowerCAmelCase__      ,  'r'			).readlines()]

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

	__UpperCAmelCase         =					1_00.0 * em / total
	logger.info(F"""Precision@{k}: {em: .2f}"""			)



def 						__snake_case (       lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     List[Any]      ,  lowerCAmelCase       :     Optional[int]			):
	def strip_title(lowerCAmelCase       :     Union[str, Any]			):
		if title.startswith('"'			):
			__UpperCAmelCase         =					title[1:]
		if title.endswith('"'			):
			__UpperCAmelCase         =					title[:-1]
		return title

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

	__UpperCAmelCase         =					rag_model.rag.question_encoder(lowerCAmelCase__			)
	__UpperCAmelCase         =					question_enc_outputs[0]

	__UpperCAmelCase         =					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         =					rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
	__UpperCAmelCase         =					[]
	for docs in all_docs:
		__UpperCAmelCase         =					[strip_title(lowerCAmelCase__			) for title in docs['title']]
		provenance_strings.append('\t'.join(lowerCAmelCase__			)			)
	return provenance_strings



def 						__snake_case (       lowerCAmelCase       :     List[str]      ,  lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     Optional[Any]			):
	with torch.no_grad():
		__UpperCAmelCase         =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    lowerCAmelCase__      ,  return_tensors='pt'      ,  padding=lowerCAmelCase__      ,  truncation=lowerCAmelCase__			)

		__UpperCAmelCase         =					inputs_dict.input_ids.to(args.device			)
		__UpperCAmelCase         =					inputs_dict.attention_mask.to(args.device			)
		__UpperCAmelCase         =					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         =					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 (       ):
	__UpperCAmelCase         =					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         =					parser.parse_args()
	__UpperCAmelCase         =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)
	return args



def 						__snake_case (       lowerCAmelCase       :     Optional[Any]			):
	__UpperCAmelCase         =					{}
	if args.model_type is None:
		__UpperCAmelCase         =					infer_model_type(args.model_name_or_path			)
		assert args.model_type is not None
	if args.model_type.startswith('rag'			):
		__UpperCAmelCase         =					RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
		__UpperCAmelCase         =					args.n_docs
		if args.index_name is not None:
			__UpperCAmelCase         =					args.index_name
		if args.index_path is not None:
			__UpperCAmelCase         =					args.index_path
	else:
		__UpperCAmelCase         =					BartForConditionalGeneration

	__UpperCAmelCase         =					(
	    [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         =					get_scores if args.eval_mode == 'e2e' else get_precision_at_k
	__UpperCAmelCase         =					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         =					RagRetriever.from_pretrained(lowerCAmelCase__      ,  **lowerCAmelCase__			)
			__UpperCAmelCase         =					model_class.from_pretrained(lowerCAmelCase__      ,  retriever=lowerCAmelCase__      ,  **lowerCAmelCase__			)
			model.retriever.init_retrieval()
		else:
			__UpperCAmelCase         =					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         =					[]
			for line in tqdm(lowerCAmelCase__			):
				questions.append(line.strip()			)
				if len(lowerCAmelCase__			) == args.eval_batch_size:
					__UpperCAmelCase         =					evaluate_batch_fn(lowerCAmelCase__      ,  lowerCAmelCase__      ,  lowerCAmelCase__			)
					preds_file.write('\n'.join(lowerCAmelCase__			) + '\n'			)
					preds_file.flush()
					__UpperCAmelCase         =					[]
			if len(lowerCAmelCase__			) > 0:
				__UpperCAmelCase         =					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__":
					_UpperCamelCase				:		Any       			=		get_args()
					main(args)




'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_UpperCamelCase				:		List[Any]       			=		get_logger(__name__)



def 						__snake_case (       lowerCAmelCase       :     List[str]			):
 if version.parse(lowerCAmelCase			) < version.parse(lowerCAmelCase			):
  if "dev" in min_version:
   __UpperCAmelCase         =					(
       'This example requires a source install from HuggingFace diffusers (see '
       '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
   )
  else:
   __UpperCAmelCase         =					F"""This example requires a minimum version of {min_version},"""
  error_message += F""" but the version found is {__version__}.\n"""
  raise ImportError(lowerCAmelCase			)



