



'''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


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

transformers_logging.set_verbosity_info()





def 						__snake_case(				_lowerCAmelCase		)		-> 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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      return max(metric_fn(_lowerCAmelCase     ,   _lowerCAmelCase		) for gt in ground_truths		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[str]:
      snake_case__  :    str    						=      [line.strip() for line in open(_lowerCAmelCase     ,   """r"""		).readlines()]
      snake_case__  :    Optional[Any]    						=      []

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

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

      snake_case__  :    Union[str, Any]    						=      100.0 * em / total
      snake_case__  :    Any    						=      100.0 * fa / total

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Union[str, Any]    						=      args.k
      snake_case__  :    Tuple    						=      [line.strip() for line in open(_lowerCAmelCase     ,   """r"""		).readlines()]
      snake_case__  :    Tuple    						=      [line.strip() for line in open(_lowerCAmelCase     ,   """r"""		).readlines()]

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

      snake_case__  :    str    						=      100.0 * em / total
      logger.info(f"Precision@{k}: {em: .2f}"		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      def strip_title(_lowerCAmelCase		):
            if title.startswith("""\""""		):
                  snake_case__  :    Tuple    						=      title[1:]
            if title.endswith("""\""""		):
                  snake_case__  :    Optional[Any]    						=      title[:-1]
            return title

      snake_case__  :    Union[str, Any]    						=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          _lowerCAmelCase     ,   return_tensors="""pt"""     ,   padding=_lowerCAmelCase     ,   truncation=_lowerCAmelCase     ,   )['''input_ids'''].to(args.device		)

      snake_case__  :    str    						=      rag_model.rag.question_encoder(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      question_enc_outputs[0]

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[str]:
      with torch.no_grad():
            snake_case__  :    Any    						=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                _lowerCAmelCase     ,   return_tensors="""pt"""     ,   padding=_lowerCAmelCase     ,   truncation=_lowerCAmelCase		)

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

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

            return answers





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





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    int    						=      {}
      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__  :    Optional[Any]    						=      RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
            snake_case__  :    Dict    						=      args.n_docs
            if args.index_name is not None:
                  snake_case__  :    Dict    						=      args.index_name
            if args.index_path is not None:
                  snake_case__  :    str    						=      args.index_path
      else:
            snake_case__  :    int    						=      BartForConditionalGeneration

      snake_case__  :    Optional[Any]    						=      (
          [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"""     ,   _lowerCAmelCase		)

      snake_case__  :    str    						=      get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
      snake_case__  :    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(_lowerCAmelCase     ,   args.predictions_path     ,   args.gold_data_path		)
                  continue

            logger.info("""***** Running evaluation for {} *****""".format(_lowerCAmelCase		)		)
            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__  :    int    						=      RagRetriever.from_pretrained(_lowerCAmelCase     ,   **_lowerCAmelCase		)
                  snake_case__  :    Optional[Any]    						=      model_class.from_pretrained(_lowerCAmelCase     ,   retriever=_lowerCAmelCase     ,   **_lowerCAmelCase		)
                  model.retriever.init_retrieval()
            else:
                  snake_case__  :    Union[str, Any]    						=      model_class.from_pretrained(_lowerCAmelCase     ,   **_lowerCAmelCase		)
            model.to(args.device		)

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

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


if __name__ == "__main__":
       __a          =		get_args()
       main(args)





'''simple docstring'''






import argparse
import os
import re

import packaging.version


UpperCamelCase__:   Union[str, Any]								=			"examples/"
UpperCamelCase__:   Optional[Any]								=			{
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
UpperCamelCase__:   Optional[int]								=			{
    "init": "src/diffusers/__init__.py",
    "setup": "setup.py",
}
UpperCamelCase__:   List[Any]								=			"README.md"


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						int  )      ->   Optional[int]:
							with open(_lowerCAmelCase   ,      '''r'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														UpperCAmelCase :       Optional[int] 							=		f.read()
							UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		REPLACE_PATTERNS[pattern]
							UpperCAmelCase :       List[Any] 							=		replace.replace('''VERSION'''   ,      _lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		re_pattern.sub(_lowerCAmelCase   ,      _lowerCAmelCase  )
							with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														f.write(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Optional[int]:
							for folder, directories, fnames in os.walk(_lowerCAmelCase  ):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove('''research_projects'''  )
														if "legacy" in directories:
																					directories.remove('''legacy'''  )
														for fname in fnames:
																					if fname.endswith('''.py'''  ):
																												update_version_in_file(os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      pattern='''examples'''  )


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						str=False  )      ->   List[str]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							if not patch:
														update_version_in_examples(_lowerCAmelCase  )


def  snake_case_     (					)      ->   Optional[Any]:
							UpperCAmelCase :       Optional[int] 							=		'''🤗 Transformers currently provides the following architectures'''
							UpperCAmelCase :       Optional[int] 							=		'''1. Want to contribute a new model?'''
							with open(_lowerCAmelCase   ,      '''r'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														UpperCAmelCase :       Optional[Any] 							=		f.readlines()

							# Find the start of the list.
							UpperCAmelCase :       List[Any] 							=		0
							while not lines[start_index].startswith(_start_prompt  ):
														start_index += 1
							start_index += 1

							UpperCAmelCase :       Optional[Any] 							=		start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt  ):
														if lines[index].startswith('''1.'''  ):
																					UpperCAmelCase :       Optional[int] 							=		lines[index].replace(
																					    '''https://huggingface.co/docs/diffusers/main/model_doc'''   ,      '''https://huggingface.co/docs/diffusers/model_doc'''   ,      )
														index += 1

							with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														f.writelines(_lowerCAmelCase  )


def  snake_case_     (					)      ->   Optional[Any]:
							with open(REPLACE_FILES['''init''']   ,      '''r'''  ) as f:
														UpperCAmelCase :       Union[str, Any] 							=		f.read()
							UpperCAmelCase :       int 							=		REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase  ).groups()[0]
							return packaging.version.parse(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]=False  )      ->   Any:
							UpperCAmelCase :       Optional[Any] 							=		get_version()
							if patch and default_version.is_devrelease:
														raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''  )
							if default_version.is_devrelease:
														UpperCAmelCase :       Optional[int] 							=		default_version.base_version
							elif patch:
														UpperCAmelCase :       Union[str, Any] 							=		f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														UpperCAmelCase :       Union[str, Any] 							=		f"""{default_version.major}.{default_version.minor + 1}.0"""

							# Now let's ask nicely if that's the right one.
							UpperCAmelCase :       Dict 							=		input(f"""Which version are you releasing? [{default_version}]"""  )
							if len(_lowerCAmelCase  ) == 0:
														UpperCAmelCase :       Tuple 							=		default_version

							print(f"""Updating version to {version}."""  )
							global_version_update(_lowerCAmelCase   ,      patch=_lowerCAmelCase  )


def  snake_case_     (					)      ->   Any:
							UpperCAmelCase :       List[Any] 							=		get_version()
							UpperCAmelCase :       List[str] 							=		f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							UpperCAmelCase :       List[Any] 							=		current_version.base_version

							# Check with the user we got that right.
							UpperCAmelCase :       Optional[int] 							=		input(f"""Which version are we developing now? [{dev_version}]"""  )
							if len(_lowerCAmelCase  ) == 0:
														UpperCAmelCase :       Dict 							=		dev_version

							print(f"""Updating version to {version}."""  )
							global_version_update(_lowerCAmelCase  )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
						UpperCamelCase__:   Union[str, Any]								=			argparse.ArgumentParser()
						parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
						parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
						UpperCamelCase__:   Optional[Any]								=			parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print("Nothing to do after a patch :-)")
						else:
												post_release_work()





