'''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


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

transformers_logging.set_verbosity_info()



def 							UpperCamelCase__			(			a__      ):


						'''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 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						return max(metric_fn(a__			,      a__      ) for gt in ground_truths      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=[line.strip() for line in open(a__			,      'r'      ).readlines()]
						_lowerCAmelCase										=[]

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

						_lowerCAmelCase										=_lowerCAmelCase										=_lowerCAmelCase										=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__      )

						_lowerCAmelCase										=100.0 * em / total
						_lowerCAmelCase										=100.0 * fa / total

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=args.k
						_lowerCAmelCase										=[line.strip() for line in open(a__			,      'r'      ).readlines()]
						_lowerCAmelCase										=[line.strip() for line in open(a__			,      'r'      ).readlines()]

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

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						def strip_title(a__      ):
												if title.startswith('"'      ):
																		_lowerCAmelCase										=title[1:]
												if title.endswith('"'      ):
																		_lowerCAmelCase										=title[:-1]
												return title

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

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

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						with torch.no_grad():
												_lowerCAmelCase										=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    a__			,      return_tensors='pt'			,      padding=a__			,      truncation=a__      )

												_lowerCAmelCase										=inputs_dict.input_ids.to(args.device      )
												_lowerCAmelCase										=inputs_dict.attention_mask.to(args.device      )
												_lowerCAmelCase										=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]]			,      )
												_lowerCAmelCase										=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 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=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=5_0			,      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.'			,      )
						_lowerCAmelCase										=parser.parse_args()
						_lowerCAmelCase										=torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
						return args



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										={}
						if args.model_type is None:
												_lowerCAmelCase										=infer_model_type(args.model_name_or_path      )
												assert args.model_type is not None
						if args.model_type.startswith('rag'      ):
												_lowerCAmelCase										=RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
												_lowerCAmelCase										=args.n_docs
												if args.index_name is not None:
																		_lowerCAmelCase										=args.index_name
												if args.index_path is not None:
																		_lowerCAmelCase										=args.index_path
						else:
												_lowerCAmelCase										=BartForConditionalGeneration

						_lowerCAmelCase										=(
						    [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__      )

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






'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__(   self		,						__A = 128		,						__A = 256		,						__A = 2_000.0		,						__A = 768		,						__A = 12		,						__A = 12		,						__A = 64		,						__A = 2048		,						__A = 0.1		,						) ->     str:
								super().__init__()

								_lowerCAmelCase										=nn.Sequential(
								    nn.Linear(__A		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						nn.Linear(d_model * 4		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						)

								_lowerCAmelCase										=nn.Embedding(__A		,						__A				)
								_lowerCAmelCase										=False

								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)

								_lowerCAmelCase										=nn.ModuleList()
								for lyr_num in range(__A				):
														# FiLM conditional T5 decoder
														_lowerCAmelCase										=DecoderLayer(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						d_ff=__A		,						dropout_rate=__A				)
														self.decoders.append(__A				)

								_lowerCAmelCase										=TaLayerNorm(__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Any:
								_lowerCAmelCase										=torch.mul(query_input.unsqueeze(-1				)		,						key_input.unsqueeze(-2				)				)
								return mask.unsqueeze(-3				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								_lowerCAmelCase										=get_timestep_embedding(
								    decoder_noise_time * self.config.max_decoder_noise_time		,						embedding_dim=self.config.d_model		,						max_period=self.config.max_decoder_noise_time		,						).to(dtype=self.dtype				)

								_lowerCAmelCase										=self.conditioning_emb(__A				).unsqueeze(1				)

								assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

								_lowerCAmelCase										=decoder_input_tokens.shape[1]

								# If we want to use relative positions for audio context, we can just offset
								# this sequence by the length of encodings_and_masks.
								_lowerCAmelCase										=torch.broadcast_to(
								    torch.arange(__A		,						device=decoder_input_tokens.device				)		,						(batch, seq_length)		,						)

								_lowerCAmelCase										=self.position_encoding(__A				)

								_lowerCAmelCase										=self.continuous_inputs_projection(__A				)
								inputs += position_encodings
								_lowerCAmelCase										=self.dropout(__A				)

								# decoder: No padding present.
								_lowerCAmelCase										=torch.ones(
								    decoder_input_tokens.shape[:2]		,						device=decoder_input_tokens.device		,						dtype=inputs.dtype				)

								# Translate encoding masks to encoder-decoder masks.
								_lowerCAmelCase										=[(x, self.encoder_decoder_mask(__A		,						__A				)) for x, y in encodings_and_masks]

								# cross attend style: concat encodings
								_lowerCAmelCase										=torch.cat([x[0] for x in encodings_and_encdec_masks]		,						dim=1				)
								_lowerCAmelCase										=torch.cat([x[1] for x in encodings_and_encdec_masks]		,						dim=-1				)

								for lyr in self.decoders:
														_lowerCAmelCase										=lyr(
														    __A		,						conditioning_emb=__A		,						encoder_hidden_states=__A		,						encoder_attention_mask=__A		,						)[0]

								_lowerCAmelCase										=self.decoder_norm(__A				)
								_lowerCAmelCase										=self.post_dropout(__A				)

								_lowerCAmelCase										=self.spec_out(__A				)
								return spec_out

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A=1E-6				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.ModuleList()

								# cond self attention: layer 0
								self.layer.append(
								    TaLayerSelfAttentionCond(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A				)				)

								# cross attention: layer 1
								self.layer.append(
								    TaLayerCrossAttention(
								        d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A		,						)				)

								# Film Cond MLP + dropout: last layer
								self.layer.append(
								    TaLayerFFCond(d_model=__A		,						d_ff=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A				)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						) ->     Any:
								_lowerCAmelCase										=self.layer[0](
								    __A		,						conditioning_emb=__A		,						attention_mask=__A		,						)

								if encoder_hidden_states is not None:
														_lowerCAmelCase										=torch.where(encoder_attention_mask > 0		,						0		,						-1E10				).to(
														    encoder_hidden_states.dtype				)

														_lowerCAmelCase										=self.layer[1](
														    __A		,						key_value_states=__A		,						attention_mask=__A		,						)

								# Apply Film Conditional Feed Forward layer
								_lowerCAmelCase										=self.layer[-1](__A		,						__A				)

								return (hidden_states,)

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaLayerNorm(__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     List[Any]:
								# pre_self_attention_layer_norm
								_lowerCAmelCase										=self.layer_norm(__A				)

								if conditioning_emb is not None:
														_lowerCAmelCase										=self.FiLMLayer(__A		,						__A				)

								# Self-attention block
								_lowerCAmelCase										=self.attention(__A				)

								_lowerCAmelCase										=hidden_states + self.dropout(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								super().__init__()
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     Tuple:
								_lowerCAmelCase										=self.layer_norm(__A				)
								_lowerCAmelCase										=self.attention(
								    __A		,						encoder_hidden_states=__A		,						attention_mask=attention_mask.squeeze(1				)		,						)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return layer_output

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaDenseGatedActDense(d_model=__A		,						d_ff=__A		,						dropout_rate=__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								_lowerCAmelCase										=self.layer_norm(__A				)
								if conditioning_emb is not None:
														_lowerCAmelCase										=self.film(__A		,						__A				)

								_lowerCAmelCase										=self.DenseReluDense(__A				)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)
								_lowerCAmelCase										=NewGELUActivation()






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=self.act(self.wi_a(__A				)				)
								_lowerCAmelCase										=self.wi_a(__A				)
								_lowerCAmelCase										=hidden_gelu * hidden_linear
								_lowerCAmelCase										=self.dropout(__A				)

								_lowerCAmelCase										=self.wo(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=1E-6				) ->     int:
								super().__init__()
								_lowerCAmelCase										=nn.Parameter(torch.ones(__A				)				)
								_lowerCAmelCase										=eps






		def 		UpperCamelCase__       (   self		,						__A				) ->     Dict:
								# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
								# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
								# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
								# half-precision inputs is done in fp32

								_lowerCAmelCase										=hidden_states.to(torch.floataa				).pow(2				).mean(-1		,						keepdim=__A				)
								_lowerCAmelCase										=hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

								# convert into half-precision if necessary
								if self.weight.dtype in [torch.floataa, torch.bfloataa]:
														_lowerCAmelCase										=hidden_states.to(self.weight.dtype				)

								return self.weight * hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(__A		,						3.0				))				))

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						out_features * 2		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase										=self.scale_bias(__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=torch.chunk(__A		,						2		,						-1				)
								_lowerCAmelCase										=x * (1 + scale) + shift
								return x





