



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


lowerCAmelCase__		:					Tuple							=logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= [line.strip() for line in open(a__							,       'r'       ).readlines()]
							__SCREAMING_SNAKE_CASE									= []

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

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

							__SCREAMING_SNAKE_CASE									= 100.0 * em / total
							__SCREAMING_SNAKE_CASE									= 100.0 * fa / total

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






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

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

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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			int:
							def strip_title(a__       ):
														if title.startswith('"'       ):
																					__SCREAMING_SNAKE_CASE									= title[1:]
														if title.endswith('"'       ):
																					__SCREAMING_SNAKE_CASE									= title[:-1]
														return title

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

							__SCREAMING_SNAKE_CASE									= rag_model.rag.question_encoder(a__       )
							__SCREAMING_SNAKE_CASE									= question_enc_outputs[0]

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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			int:
							with torch.no_grad():
														__SCREAMING_SNAKE_CASE									= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    a__							,       return_tensors='pt'							,       padding=a__							,       truncation=a__       )

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

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

														return answers






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






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

							__SCREAMING_SNAKE_CASE									= (
							    [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'							,       a__       )

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

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

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

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


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











import functools
import operator

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


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''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_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_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=0.0_5				,							_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				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= apply_spec_augment
													__SCREAMING_SNAKE_CASE									= mask_time_prob
													__SCREAMING_SNAKE_CASE									= mask_time_length
													__SCREAMING_SNAKE_CASE									= mask_time_min_masks
													__SCREAMING_SNAKE_CASE									= mask_feature_prob
													__SCREAMING_SNAKE_CASE									= mask_feature_length
													__SCREAMING_SNAKE_CASE									= mask_feature_min_masks

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return functools.reduce(operator.mul				,							self.conv_stride				,							1		)






