
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


SCREAMING_SNAKE_CASE__							=    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						List[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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Tuple:
				return max(metric_fn(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for gt in ground_truths      )




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				List[str]      )					->						Tuple:
				__lowercase								= [line.strip() for line in open(SCREAMING_SNAKE_CASE   ,							'r'      ).readlines()]
				__lowercase								= []

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

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

				__lowercase								= 100.0 * em / total
				__lowercase								= 100.0 * fa / total

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any]      )					->						List[str]:
				__lowercase								= args.k
				__lowercase								= [line.strip() for line in open(SCREAMING_SNAKE_CASE   ,							'r'      ).readlines()]
				__lowercase								= [line.strip() for line in open(SCREAMING_SNAKE_CASE   ,							'r'      ).readlines()]

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

				__lowercase								= 100.0 * em / total
				logger.info(F"""Precision@{k}: {em: .2f}"""      )




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				List[Any]      )					->						Optional[int]:
				def strip_title(SCREAMING_SNAKE_CASE	:				str      ):
								if title.startswith('"'      ):
												__lowercase								= title[1:]
								if title.endswith('"'      ):
												__lowercase								= title[:-1]
								return title

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

				__lowercase								= rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE      )
				__lowercase								= question_enc_outputs[0]

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				Dict      )					->						Union[str, Any]:
				with torch.no_grad():
								__lowercase								= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    SCREAMING_SNAKE_CASE   ,							return_tensors='pt'   ,							padding=SCREAMING_SNAKE_CASE   ,							truncation=SCREAMING_SNAKE_CASE      )

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

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

								return answers




def 							__SCREAMING_SNAKE_CASE       (		)					->						List[Any]:
				__lowercase								= argparse.ArgumentParser()
				parser.add_argument(
				    '--model_type'   ,							choices=['rag_sequence', 'rag_token', 'bart']   ,							type=SCREAMING_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=SCREAMING_SNAKE_CASE   ,							choices=['exact', 'compressed', 'legacy']   ,							type=SCREAMING_SNAKE_CASE   ,							help='RAG model retriever type'   ,							)
				parser.add_argument(
				    '--index_path'   ,							default=SCREAMING_SNAKE_CASE   ,							type=SCREAMING_SNAKE_CASE   ,							help='Path to the retrieval index'   ,							)
				parser.add_argument('--n_docs'   ,							default=5   ,							type=SCREAMING_SNAKE_CASE   ,							help='Number of retrieved docs'      )
				parser.add_argument(
				    '--model_name_or_path'   ,							default=SCREAMING_SNAKE_CASE   ,							type=SCREAMING_SNAKE_CASE   ,							required=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE   ,							help='k for the precision@k calculation'      )
				parser.add_argument(
				    '--evaluation_set'   ,							default=SCREAMING_SNAKE_CASE   ,							type=SCREAMING_SNAKE_CASE   ,							required=SCREAMING_SNAKE_CASE   ,							help='Path to a file containing evaluation samples'   ,							)
				parser.add_argument(
				    '--gold_data_path'   ,							default=SCREAMING_SNAKE_CASE   ,							type=SCREAMING_SNAKE_CASE   ,							required=SCREAMING_SNAKE_CASE   ,							help='Path to a tab-separated file with gold samples'   ,							)
				parser.add_argument(
				    '--gold_data_mode'   ,							default='qa'   ,							type=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE   ,							help='Number of beams to be used when generating answers'   ,							)
				parser.add_argument('--min_length'   ,							default=1   ,							type=SCREAMING_SNAKE_CASE   ,							help='Min length of the generated answers'      )
				parser.add_argument('--max_length'   ,							default=50   ,							type=SCREAMING_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.'   ,							)
				__lowercase								= parser.parse_args()
				__lowercase								= torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
				return args




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						Dict:
				__lowercase								= {}
				if args.model_type is None:
								__lowercase								= infer_model_type(args.model_name_or_path      )
								assert args.model_type is not None
				if args.model_type.startswith('rag'      ):
								__lowercase								= RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
								__lowercase								= args.n_docs
								if args.index_name is not None:
												__lowercase								= args.index_name
								if args.index_path is not None:
												__lowercase								= args.index_path
				else:
								__lowercase								= BartForConditionalGeneration

				__lowercase								= (
				    [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'   ,							SCREAMING_SNAKE_CASE      )

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

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

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__							=    get_args()
							main(args)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


											super().__init__(pad_token_id=_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											if self.task == "multiple-choice":
															__lowercase								= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]    )





