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

transformers_logging.set_verbosity_info()
def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''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__							(    __magic_name__  :       List[str]							,					__magic_name__  :       str							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					return max(metric_fn(__magic_name__							,					__magic_name__					) for gt in ground_truths					)
def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       [line.strip() for line in open(__magic_name__							,					'''r'''					).readlines()]
					lowerCAmelCase						:   str							=       []

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

					lowerCAmelCase						:   List[str]							=       0
					for prediction, ground_truths in zip(__magic_name__							,					__magic_name__					):
										total += 1
										em += metric_max_over_ground_truths(__magic_name__							,					__magic_name__							,					__magic_name__					)
										fa += metric_max_over_ground_truths(__magic_name__							,					__magic_name__							,					__magic_name__					)

					lowerCAmelCase						:   Tuple							=       100.0 * em / total
					lowerCAmelCase						:   List[str]							=       100.0 * fa / total

					logger.info(f'''F1: {fa:.2f}'''					)
					logger.info(f'''EM: {em:.2f}'''					)
def       UpperCAmelCase__							(    __magic_name__  :       Optional[int]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Tuple					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       args.k
					lowerCAmelCase						:   List[str]							=       [line.strip() for line in open(__magic_name__							,					'''r'''					).readlines()]
					lowerCAmelCase						:   int							=       [line.strip() for line in open(__magic_name__							,					'''r'''					).readlines()]

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

					lowerCAmelCase						:   Union[str, Any]							=       100.0 * em / total
					logger.info(f'''Precision@{k}: {em: .2f}'''					)
def       UpperCAmelCase__							(    __magic_name__  :       Dict							,					__magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					def strip_title(__magic_name__  :       Union[str, Any]					):
										if title.startswith('''"'''					):
															lowerCAmelCase						:   Dict							=       title[1:]
										if title.endswith('''"'''					):
															lowerCAmelCase						:   Any							=       title[:-1]
										return title

					lowerCAmelCase						:   Tuple							=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    __magic_name__							,					return_tensors='''pt'''							,					padding=__magic_name__							,					truncation=__magic_name__							,					)['''input_ids'''].to(args.device					)

					lowerCAmelCase						:   str							=       rag_model.rag.question_encoder(__magic_name__					)
					lowerCAmelCase						:   int							=       question_enc_outputs[0]

					lowerCAmelCase						:   str							=       rag_model.retriever(
					    __magic_name__							,					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						:   Any							=       rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
					lowerCAmelCase						:   int							=       []
					for docs in all_docs:
										lowerCAmelCase						:   List[Any]							=       [strip_title(__magic_name__					) for title in docs['''title''']]
										provenance_strings.append('''\t'''.join(__magic_name__					)					)
					return provenance_strings
def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       Dict							,					__magic_name__  :       str					):


					'''simple docstring'''

					with torch.no_grad():
										lowerCAmelCase						:   Any							=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    __magic_name__							,					return_tensors='''pt'''							,					padding=__magic_name__							,					truncation=__magic_name__					)

										lowerCAmelCase						:   Optional[Any]							=       inputs_dict.input_ids.to(args.device					)
										lowerCAmelCase						:   Dict							=       inputs_dict.attention_mask.to(args.device					)
										lowerCAmelCase						:   Any							=       rag_model.generate(  # rag_model overwrites generate
										    __magic_name__							,					attention_mask=__magic_name__							,					num_beams=args.num_beams							,					min_length=args.min_length							,					max_length=args.max_length							,					early_stopping=__magic_name__							,					num_return_sequences=1							,					bad_words_ids=[[0, 0]]							,					)
										lowerCAmelCase						:   List[Any]							=       rag_model.retriever.generator_tokenizer.batch_decode(__magic_name__							,					skip_special_tokens=__magic_name__					)

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

										return answers
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

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


					'''simple docstring'''

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

					lowerCAmelCase						:   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'''							,					__magic_name__					)

					lowerCAmelCase						:   List[Any]							=       get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
					lowerCAmelCase						:   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(__magic_name__							,					args.predictions_path							,					args.gold_data_path					)
															continue

										logger.info('''***** Running evaluation for {} *****'''.format(__magic_name__					)					)
										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						:   str							=       RagRetriever.from_pretrained(__magic_name__							,					**__magic_name__					)
															lowerCAmelCase						:   List[str]							=       model_class.from_pretrained(__magic_name__							,					retriever=__magic_name__							,					**__magic_name__					)
															model.retriever.init_retrieval()
										else:
															lowerCAmelCase						:   int							=       model_class.from_pretrained(__magic_name__							,					**__magic_name__					)
										model.to(args.device					)

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

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       get_args()
		main(args)


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :				int             =       {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"gpt2"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple=5_0_2_5_7  ,					lowerCamelCase__      :		str=1_0_2_4  ,					lowerCamelCase__      :		Union[str, Any]=7_6_8  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Tuple="gelu_new"  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		str=0.1  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		Dict=1E-5  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		Tuple="cls_index"  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		int=True  ,					lowerCamelCase__      :		Tuple=0.1  ,					lowerCamelCase__      :		List[str]=True  ,					lowerCamelCase__      :		Dict=True  ,					lowerCamelCase__      :		Tuple=5_0_2_5_6  ,					lowerCamelCase__      :		Optional[int]=5_0_2_5_6  ,					lowerCamelCase__      :		Dict=False  ,					lowerCamelCase__      :		Tuple=False  ,					**lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       vocab_size
							lowerCAmelCase						:   Dict							=       n_positions
							lowerCAmelCase						:   int							=       n_embd
							lowerCAmelCase						:   Union[str, Any]							=       n_layer
							lowerCAmelCase						:   List[str]							=       n_head
							lowerCAmelCase						:   str							=       n_inner
							lowerCAmelCase						:   Tuple							=       activation_function
							lowerCAmelCase						:   List[Any]							=       resid_pdrop
							lowerCAmelCase						:   Any							=       embd_pdrop
							lowerCAmelCase						:   int							=       attn_pdrop
							lowerCAmelCase						:   Dict							=       layer_norm_epsilon
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       summary_type
							lowerCAmelCase						:   int							=       summary_use_proj
							lowerCAmelCase						:   List[str]							=       summary_activation
							lowerCAmelCase						:   List[str]							=       summary_first_dropout
							lowerCAmelCase						:   Optional[int]							=       summary_proj_to_labels
							lowerCAmelCase						:   Optional[int]							=       scale_attn_weights
							lowerCAmelCase						:   List[str]							=       use_cache
							lowerCAmelCase						:   List[Any]							=       scale_attn_by_inverse_layer_idx
							lowerCAmelCase						:   int							=       reorder_and_upcast_attn

							lowerCAmelCase						:   List[Any]							=       bos_token_id
							lowerCAmelCase						:   Optional[int]							=       eos_token_id

							super().__init__(bos_token_id=lowerCamelCase__  ,					eos_token_id=lowerCamelCase__  ,					**lowerCamelCase__			)



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		PretrainedConfig  ,					lowerCamelCase__      :		str = "default"  ,					lowerCamelCase__      :		List[PatchingSpec] = None  ,					lowerCamelCase__      :		bool = False  ,					):
							super().__init__(lowerCamelCase__  ,					task=lowerCamelCase__  ,					patching_specs=lowerCamelCase__  ,					use_past=lowerCamelCase__			)
							if not getattr(self._config  ,					'''pad_token_id'''  ,					lowerCamelCase__			):
												# TODO: how to do that better?
												lowerCAmelCase						:   str							=       0





		@property
		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[Any]							=       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
							if self.use_past:
												self.fill_with_past_key_values_(lowerCamelCase__  ,					direction='''inputs'''			)
												lowerCAmelCase						:   str							=       {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												lowerCAmelCase						:   int							=       {0: '''batch''', 1: '''sequence'''}

							return common_inputs





		@property
		def  _A							(							self      :		Optional[int]			):
							return self._config.n_layer





		@property
		def  _A							(							self      :		List[str]			):
							return self._config.n_head





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		PreTrainedTokenizer  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		bool = False  ,					lowerCamelCase__      :		Optional[TensorType] = None  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       super(lowerCamelCase__  ,					self			).generate_dummy_inputs(
							    lowerCamelCase__  ,					batch_size=lowerCamelCase__  ,					seq_length=lowerCamelCase__  ,					is_pair=lowerCamelCase__  ,					framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase						:   Tuple							=       OrderedDict({'''input_ids''': common_inputs['''input_ids''']}			)

							# Need to add the past_keys
							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
												else:
																	import torch

																	lowerCAmelCase		,							lowerCAmelCase						:   Any							=       common_inputs['''input_ids'''].shape
																	# Not using the same length for past_key_values
																	lowerCAmelCase						:   Optional[int]							=       seqlen + 2
																	lowerCAmelCase						:   Any							=       (
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	lowerCAmelCase						:   Any							=       [
																	    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																	]

							lowerCAmelCase						:   List[str]							=       common_inputs['''attention_mask''']
							if self.use_past:
												lowerCAmelCase						:   Optional[Any]							=       ordered_inputs['''attention_mask'''].dtype
												lowerCAmelCase						:   Optional[Any]							=       torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase__  ,					lowerCamelCase__  ,					dtype=lowerCamelCase__			)]  ,					dim=1			)

							return ordered_inputs





		@property
		def  _A							(							self      :		Union[str, Any]			):
							return 1_3
