

'''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


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

transformers_logging.set_verbosity_info()
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       return max(metric_fn(UpperCAmelCase__,							UpperCAmelCase__					) for gt in ground_truths					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       A_						       =   [line.strip() for line in open(UpperCAmelCase__,							"""r"""					).readlines()]
       A_						       =   []

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

       A_						       =   A_						       =   A_						       =   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__					)

       A_						       =   100.0 * em / total
       A_						       =   100.0 * fa / total

       logger.info(F'''F1: {fa:.2f}'''					)
       logger.info(F'''EM: {em:.2f}'''					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      str:
       A_						       =   args.k
       A_						       =   [line.strip() for line in open(UpperCAmelCase__,							"""r"""					).readlines()]
       A_						       =   [line.strip() for line in open(UpperCAmelCase__,							"""r"""					).readlines()]

       A_						       =   A_						       =   0
       for hypo, reference in zip(UpperCAmelCase__,							UpperCAmelCase__					):
              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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       def strip_title(UpperCAmelCase__					):
              if title.startswith("""\""""					):
                     A_						       =   title[1:]
              if title.endswith("""\""""					):
                     A_						       =   title[:-1]
              return title

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

       A_						       =   rag_model.rag.question_encoder(UpperCAmelCase__					)
       A_						       =   question_enc_outputs[0]

       A_						       =   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""",							)
       A_						       =   rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       A_						       =   []
       for docs in all_docs:
              A_						       =   [strip_title(UpperCAmelCase__					) for title in docs["""title"""]]
              provenance_strings.append("""\t""".join(UpperCAmelCase__					)					)
       return provenance_strings
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       with torch.no_grad():
              A_						       =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  UpperCAmelCase__,							return_tensors="""pt""",							padding=UpperCAmelCase__,							truncation=UpperCAmelCase__					)

              A_						       =   inputs_dict.input_ids.to(args.device					)
              A_						       =   inputs_dict.attention_mask.to(args.device					)
              A_						       =   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]],							)
              A_						       =   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    UpperCAmelCase__     (   )      ->      Union[str, Any]:
       A_						       =   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.""",							)
       A_						       =   parser.parse_args()
       A_						       =   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
       return args
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       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""",							UpperCAmelCase__					)

       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(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"""					):
                     A_						       =   RagRetriever.from_pretrained(UpperCAmelCase__,							**UpperCAmelCase__					)
                     A_						       =   model_class.from_pretrained(UpperCAmelCase__,							retriever=UpperCAmelCase__,							**UpperCAmelCase__					)
                     model.retriever.init_retrieval()
              else:
                     A_						       =   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:
                     A_						       =   []
                     for line in tqdm(UpperCAmelCase__					):
                            questions.append(line.strip()					)
                            if len(UpperCAmelCase__					) == args.eval_batch_size:
                                   A_						       =   evaluate_batch_fn(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)
                                   preds_file.write("""\n""".join(UpperCAmelCase__					) + """\n"""					)
                                   preds_file.flush()
                                   A_						       =   []
                     if len(UpperCAmelCase__					) > 0:
                            A_						       =   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									=     get_args()
      main(args)



'''simple docstring'''







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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__="attention"					)      ->      Dict:
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      Optional[Any]:
       if split_mlp_wi:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
              A_						       =   (wi_a, wi_a)
       else:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

       A_						       =   params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def    UpperCAmelCase__     (   UpperCAmelCase__,							*, UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   traverse_util.flatten_dict(variables["""target"""]					)
       A_						       =   {"""/""".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
       A_						       =   """encoder/layers_0/mlp/wi_0/kernel""" in old
       print("""Split MLP:""",							UpperCAmelCase__					)

       A_						       =   collections.OrderedDict()

       # Shared embeddings.
       A_						       =   old["""token_embedder/embedding"""]

       # Encoder.
       for i in range(UpperCAmelCase__					):
              # Block i, layer 0 (Self Attention).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_attention_layer_norm"""					)
              A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""attention"""					)
              A_						       =   layer_norm
              A_						       =   k.T
              A_						       =   o.T
              A_						       =   q.T
              A_						       =   v.T

              # Block i, layer 1 (MLP).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_mlp_layer_norm"""					)
              A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							UpperCAmelCase__					)
              A_						       =   layer_norm
              if split_mlp_wi:
                     A_						       =   wi[0].T
                     A_						       =   wi[1].T
              else:
                     A_						       =   wi.T
              A_						       =   wo.T

       A_						       =   old[
           """encoder/relpos_bias/rel_embedding"""
       ].T
       A_						       =   old["""encoder/encoder_norm/scale"""]

       if not is_encoder_only:
              # Decoder.
              for i in range(UpperCAmelCase__					):
                     # Block i, layer 0 (Self Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_self_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""self_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 1 (Cross Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_cross_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""encoder_decoder_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 2 (MLP).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_mlp_layer_norm"""					)
                     A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							UpperCAmelCase__					)
                     A_						       =   layer_norm
                     if split_mlp_wi:
                            A_						       =   wi[0].T
                            A_						       =   wi[1].T
                     else:
                            A_						       =   wi.T
                     A_						       =   wo.T

              A_						       =   old["""decoder/decoder_norm/scale"""]
              A_						       =   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:
                     A_						       =   old["""decoder/logits_dense/kernel"""].T

       return new
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   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:
              A_						       =   state_dict["""shared.weight"""]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     A_						       =   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."""					)
                     A_						       =   state_dict["""shared.weight"""]

       return state_dict
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   checkpoints.load_tax_checkpoint(UpperCAmelCase__					)
       A_						       =   convert_tax_to_pytorch(UpperCAmelCase__,							num_layers=config.num_layers,							is_encoder_only=UpperCAmelCase__					)
       A_						       =   make_state_dict(UpperCAmelCase__,							UpperCAmelCase__					)
       model.load_state_dict(UpperCAmelCase__,							strict=UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = False					)      ->      List[Any]:
       A_						       =   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:
              A_						       =   TaEncoderModel(UpperCAmelCase__					)
       else:
              A_						       =   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									=     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									=     parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )
