





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

transformers_logging.set_verbosity_info()







def    A				(							UpperCAmelCase				):
					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				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return max(metric_fn(UpperCAmelCase  ,  UpperCAmelCase				) for gt in ground_truths				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[Any]				       =   [line.strip() for line in open(UpperCAmelCase  ,  "r"				).readlines()]
					_snake_case       :					Union[str, Any]				       =   []

					if args.gold_data_mode == "qa":
										_snake_case       :					Dict				       =   pd.read_csv(UpperCAmelCase  ,  sep="\t"  ,  header=UpperCAmelCase				)
										for answer_list in data[1]:
															_snake_case       :					Union[str, Any]				       =   ast.literal_eval(UpperCAmelCase				)
															answers.append(UpperCAmelCase				)
					else:
										_snake_case       :					List[Any]				       =   [line.strip() for line in open(UpperCAmelCase  ,  "r"				).readlines()]
										_snake_case       :					Optional[int]				       =   [[reference] for reference in references]

					_snake_case       :					Optional[int]				       =   0
					for prediction, ground_truths in zip(UpperCAmelCase  ,  UpperCAmelCase				):
										total += 1
										em += metric_max_over_ground_truths(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)
										fa += metric_max_over_ground_truths(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					_snake_case       :					int				       =   1_0_0.0 * em / total
					_snake_case       :					Any				       =   1_0_0.0 * fa / total

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







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[Any]				       =   args.k
					_snake_case       :					Optional[int]				       =   [line.strip() for line in open(UpperCAmelCase  ,  "r"				).readlines()]
					_snake_case       :					Optional[int]				       =   [line.strip() for line in open(UpperCAmelCase  ,  "r"				).readlines()]

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

					_snake_case       :					Optional[int]				       =   1_0_0.0 * em / total
					logger.info(F"""Precision@{k}: {em: .2f}"""				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					def strip_title(UpperCAmelCase				):
										if title.startswith("\""				):
															_snake_case       :					str				       =   title[1:]
										if title.endswith("\""				):
															_snake_case       :					int				       =   title[:-1]
										return title

					_snake_case       :					Optional[Any]				       =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    UpperCAmelCase  ,  return_tensors="pt"  ,  padding=UpperCAmelCase  ,  truncation=UpperCAmelCase  ,  )["input_ids"].to(args.device				)

					_snake_case       :					int				       =   rag_model.rag.question_encoder(UpperCAmelCase				)
					_snake_case       :					int				       =   question_enc_outputs[0]

					_snake_case       :					Optional[Any]				       =   rag_model.retriever(
					    UpperCAmelCase  ,  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"  ,  )
					_snake_case       :					List[Any]				       =   rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
					_snake_case       :					Tuple				       =   []
					for docs in all_docs:
										_snake_case       :					str				       =   [strip_title(UpperCAmelCase				) for title in docs["title"]]
										provenance_strings.append("\t".join(UpperCAmelCase				)				)
					return provenance_strings







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					with torch.no_grad():
										_snake_case       :					str				       =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    UpperCAmelCase  ,  return_tensors="pt"  ,  padding=UpperCAmelCase  ,  truncation=UpperCAmelCase				)

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

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

										return answers







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







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

					_snake_case       :					Dict				       =   (
					    [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"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
					_snake_case       :					List[str]				       =   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(UpperCAmelCase  ,  args.predictions_path  ,  args.gold_data_path				)
															continue

										logger.info("***** Running evaluation for {} *****".format(UpperCAmelCase				)				)
										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"				):
															_snake_case       :					Optional[Any]				       =   RagRetriever.from_pretrained(UpperCAmelCase  ,  **UpperCAmelCase				)
															_snake_case       :					Tuple				       =   model_class.from_pretrained(UpperCAmelCase  ,  retriever=UpperCAmelCase  ,  **UpperCAmelCase				)
															model.retriever.init_retrieval()
										else:
															_snake_case       :					str				       =   model_class.from_pretrained(UpperCAmelCase  ,  **UpperCAmelCase				)
										model.to(args.device				)

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

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


if __name__ == "__main__":
							__lowerCAmelCase :Optional[Any]     =    get_args()
							main(args)






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="attention"				):
					_snake_case       :					Any				       =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_snake_case       :					str				       =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										_snake_case       :					Union[str, Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Optional[int]				       =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					_snake_case       :					List[str]				       =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					str				       =   {"/".join(UpperCAmelCase				): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_snake_case       :					Tuple				       =   "encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Tuple				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					Optional[Any]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Optional[Any]				       =   layer_norm
										_snake_case       :					int				       =   k.T
										_snake_case       :					Optional[int]				       =   o.T
										_snake_case       :					Dict				       =   q.T
										_snake_case       :					Any				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					Any				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Tuple				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					Any				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					List[Any]				       =   wi.T
										_snake_case       :					Dict				       =   wo.T

					_snake_case       :					Dict				       =   old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					_snake_case       :					Dict				       =   old["encoder/encoder_norm/scale"]

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCAmelCase				):
															# Block i, layer 0 (Self Attention).
															_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_self_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Optional[Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "self_attention"				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															_snake_case       :					Dict				       =   k.T
															_snake_case       :					List[str]				       =   o.T
															_snake_case       :					List[Any]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Tuple				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Union[str, Any]				       =   k.T
															_snake_case       :					str				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					Union[str, Any]				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					str				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

										_snake_case       :					Optional[int]				       =   old["decoder/decoder_norm/scale"]
										_snake_case       :					int				       =   old[
										    "decoder/relpos_bias/rel_embedding"
										].T

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_snake_case       :					Tuple				       =   old["decoder/logits_dense/kernel"].T

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_snake_case       :					Tuple				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					List[Any]				       =   state_dict["shared.weight"]

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print("Using shared word embeddings as lm_head."				)
															_snake_case       :					Optional[int]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					Dict				       =   convert_tax_to_pytorch(UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase				)
					_snake_case       :					Dict				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					str				       =   TaConfig.from_json_file(UpperCAmelCase				)
					print(F"""Building PyTorch model from configuration: {config}"""				)
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_snake_case       :					List[Any]				       =   TaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					List[str]				       =   TaForConditionalGeneration(UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
					model.save_pretrained(UpperCAmelCase				)

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCAmelCase				)
					print("Done"				)


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
							# Required parameters
							parser.add_argument(
							    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
							)
							__lowerCAmelCase :Optional[int]     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)