



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:				Any       =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def 					a(   A :					Union[str, Any] )	-> Any:
					"""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 					a(   A :					Any				,  A :					str				,  A :					str )	-> Any:
					"""simple docstring"""





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






def 					a(   A :					List[Any]				,  A :					Any				,  A :					Tuple )	-> List[str]:
					"""simple docstring"""





					a        =     [line.strip() for line in open(snake_case__				,  "r" ).readlines()]
					a        =     []

					if args.gold_data_mode == "qa":
										a        =     pd.read_csv(snake_case__				,  sep="\t"				,  header=snake_case__ )
										for answer_list in data[1]:
															a        =     ast.literal_eval(snake_case__ )
															answers.append(snake_case__ )
					else:
										a        =     [line.strip() for line in open(snake_case__				,  "r" ).readlines()]
										a        =     [[reference] for reference in references]

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

					a        =     100.0 * em / total
					a        =     100.0 * fa / total

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






def 					a(   A :					int				,  A :					List[str]				,  A :					int )	-> str:
					"""simple docstring"""





					a        =     args.k
					a        =     [line.strip() for line in open(snake_case__				,  "r" ).readlines()]
					a        =     [line.strip() for line in open(snake_case__				,  "r" ).readlines()]

					a        =     a        =     0
					for hypo, reference in zip(snake_case__				,  snake_case__ ):
										a        =     set(hypo.split("\t" )[:k] )
										a        =     set(reference.split("\t" ) )
										total += 1
										em += len(hypo_provenance & ref_provenance ) / k

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






def 					a(   A :					Optional[Any]				,  A :					Any				,  A :					List[Any] )	-> Optional[int]:
					"""simple docstring"""





					def strip_title(A :					List[Any] ):
										if title.startswith("\"" ):
															a        =     title[1:]
										if title.endswith("\"" ):
															a        =     title[:-1]
										return title

					a        =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    snake_case__				,  return_tensors="pt"				,  padding=snake_case__				,  truncation=snake_case__				,  )["input_ids"].to(args.device )

					a        =     rag_model.rag.question_encoder(snake_case__ )
					a        =     question_enc_outputs[0]

					a        =     rag_model.retriever(
					    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"				,  )
					a        =     rag_model.retriever.index.get_doc_dicts(result.doc_ids )
					a        =     []
					for docs in all_docs:
										a        =     [strip_title(snake_case__ ) for title in docs["title"]]
										provenance_strings.append("\t".join(snake_case__ ) )
					return provenance_strings






def 					a(   A :					List[str]				,  A :					int				,  A :					Any )	-> Dict:
					"""simple docstring"""





					with torch.no_grad():
										a        =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    snake_case__				,  return_tensors="pt"				,  padding=snake_case__				,  truncation=snake_case__ )

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

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

										return answers






def 					a(   )	-> Optional[int]:
					"""simple docstring"""





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






def 					a(   A :					Optional[Any] )	-> Any:
					"""simple docstring"""





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

					a        =     (
					    [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"				,  snake_case__ )

					a        =     get_scores if args.eval_mode == "e2e" else get_precision_at_k
					a        =     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(snake_case__				,  args.predictions_path				,  args.gold_data_path )
															continue

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

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

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


if __name__ == "__main__":
				_lowercase:				Optional[int]       =	get_args()
				main(args)





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	, [0.0002, 0.002, 0.02]	):
											self.check_over_configs(beta_start=lowerCamelCase_	, beta_end=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 108.4439	) < 1E-2
						assert abs(result_mean.item() - 0.1412	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for t in scheduler.timesteps:
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3
