






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			:	Optional[int]								=logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def  a__ (__lowercase							:int   )		-> Optional[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  a__ (__lowercase							:Optional[int]					,  __lowercase							:str					,  __lowercase							:Optional[int]   )		-> Dict:
				return max(metric_fn(__lowercase					,  __lowercase   ) for gt in ground_truths   )



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

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

				_A	:				Optional[int]   =      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   )

				_A	:				Dict   =      100.0 * em / total
				_A	:				str   =      100.0 * fa / total

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



def  a__ (__lowercase							:List[Any]					,  __lowercase							:Optional[Any]					,  __lowercase							:Any   )		-> str:
				_A	:				str   =      args.k
				_A	:				Optional[int]   =      [line.strip() for line in open(__lowercase					,  '''r'''   ).readlines()]
				_A	:				List[str]   =      [line.strip() for line in open(__lowercase					,  '''r'''   ).readlines()]

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

				_A	:				List[str]   =      100.0 * em / total
				logger.info(f"""Precision@{k}: {em: .2f}"""   )



def  a__ (__lowercase							:Tuple					,  __lowercase							:Any					,  __lowercase							:Union[str, Any]   )		-> Dict:
				def strip_title(__lowercase							:Dict   ):
								if title.startswith('''"'''   ):
												_A	:				int   =      title[1:]
								if title.endswith('''"'''   ):
												_A	:				List[str]   =      title[:-1]
								return title

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

				_A	:				Optional[Any]   =      rag_model.rag.question_encoder(__lowercase   )
				_A	:				List[Any]   =      question_enc_outputs[0]

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



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

								_A	:				Dict   =      inputs_dict.input_ids.to(args.device   )
								_A	:				str   =      inputs_dict.attention_mask.to(args.device   )
								_A	:				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]]					,  )
								_A	:				Dict   =      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__ ()		-> int:
				_A	:				List[Any]   =      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=50					,  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.'''					,  )
				_A	:				Optional[int]   =      parser.parse_args()
				_A	:				Dict   =      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
				return args



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

				_A	:				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'''					,  __lowercase   )

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











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "sew"







		def __init__(					self       ,A__=32       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__=2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=0.1       ,A__=0.0       ,A__=0.1       ,A__=0.1       ,A__=0.02       ,A__=1E-5       ,A__="group"       ,A__="gelu"       ,A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,A__=False       ,A__=128       ,A__=16       ,A__=True       ,A__=0.05       ,A__=10       ,A__=2       ,A__=0.0       ,A__=10       ,A__=0       ,A__="mean"       ,A__=False       ,A__=False       ,A__=256       ,A__=0       ,A__=1       ,A__=2       ,**A__       ,):
						super().__init__(**A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__		)
						_A	:				str   =      hidden_size
						_A	:				Optional[Any]   =      feat_extract_norm
						_A	:				Any   =      feat_extract_activation
						_A	:				Optional[int]   =      list(A__		)
						_A	:				str   =      list(A__		)
						_A	:				Optional[Any]   =      list(A__		)
						_A	:				List[Any]   =      conv_bias
						_A	:				Optional[int]   =      num_conv_pos_embeddings
						_A	:				Any   =      num_conv_pos_embedding_groups
						_A	:				Union[str, Any]   =      len(self.conv_dim		)
						_A	:				List[Any]   =      num_hidden_layers
						_A	:				str   =      intermediate_size
						_A	:				List[Any]   =      squeeze_factor
						_A	:				Tuple   =      hidden_act
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				Optional[int]   =      hidden_dropout
						_A	:				Optional[int]   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				Tuple   =      feat_proj_dropout
						_A	:				Optional[Any]   =      final_dropout
						_A	:				str   =      layerdrop
						_A	:				int   =      layer_norm_eps
						_A	:				List[str]   =      initializer_range
						_A	:				List[Any]   =      vocab_size

						if (
						    (len(self.conv_stride		) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
						    or (len(self.conv_dim		) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    '''Configuration for convolutional layers is incorrect.'''
										    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A	:				Union[str, Any]   =      apply_spec_augment
						_A	:				str   =      mask_time_prob
						_A	:				Any   =      mask_time_length
						_A	:				int   =      mask_time_min_masks
						_A	:				str   =      mask_feature_prob
						_A	:				Optional[Any]   =      mask_feature_length
						_A	:				Dict   =      mask_feature_min_masks

						# ctc loss
						_A	:				Optional[int]   =      ctc_loss_reduction
						_A	:				List[str]   =      ctc_zero_infinity

						# sequence classification
						_A	:				List[Any]   =      use_weighted_layer_sum
						_A	:				Optional[int]   =      classifier_proj_size







		@property
		def  A__      (					self		):
						return functools.reduce(operator.mul       ,self.conv_stride       ,1		)



