





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

transformers_logging.set_verbosity_info()

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]				)	->				Union[str, 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      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:Optional[int]				)	->				List[str]:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:Dict				)	->				Optional[Any]:
     '''simple docstring'''


     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							:int  ,						_UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Any				)	->				str:
     '''simple docstring'''


     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							:Optional[Any]  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:Union[str, Any]				)	->				List[str]:
     '''simple docstring'''


     def strip_title(_UpperCAmelCase							: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(
         _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							:Tuple  ,						_UpperCAmelCase							:Optional[Any]  ,						_UpperCAmelCase							:str				)	->				Optional[Any]:
     '''simple docstring'''


     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_    (    )	->				List[str]:
     '''simple docstring'''


     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							:Optional[Any]				)	->				Dict:
     '''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'''  ,						_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__":
      a__      :     str         =		get_args()
      main(args)










import argparse
import os
import re


a__      :     str         =		'src/transformers'

# Pattern that looks at the indentation in a line.
a__      :     Union[str, Any]         =		re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a__      :     List[Any]         =		re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a__      :     Dict         =		re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
a__      :     Optional[int]         =		re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a__      :     Optional[Any]         =		re.compile(R'\[([^\]]+)\]')

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       _re_indent.search(_UpperCAmelCase				)
     return "" if search is None else search.groups()[0]

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:List[Any]=""  ,						_UpperCAmelCase							:Optional[Any]=None  ,						_UpperCAmelCase							:List[str]=None				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       0
     A_          =       code.split('''\n'''				)
     if start_prompt is not None:
          while not lines[index].startswith(_UpperCAmelCase				):
               index += 1
          A_          =       ['''\n'''.join(lines[:index]				)]
     else:
          A_          =       []

     # We split into blocks until we get to the `end_prompt` (or the end of the block).
     A_          =       [lines[index]]
     index += 1
     while index < len(_UpperCAmelCase				) and (end_prompt is None or not lines[index].startswith(_UpperCAmelCase				)):
          if len(lines[index]				) > 0 and get_indent(lines[index]				) == indent_level:
               if len(_UpperCAmelCase				) > 0 and get_indent(current_block[-1]				).startswith(indent_level + ''' '''				):
                    current_block.append(lines[index]				)
                    blocks.append('''\n'''.join(_UpperCAmelCase				)				)
                    if index < len(_UpperCAmelCase				) - 1:
                         A_          =       [lines[index + 1]]
                         index += 1
                    else:
                         A_          =       []
               else:
                    blocks.append('''\n'''.join(_UpperCAmelCase				)				)
                    A_          =       [lines[index]]
          else:
               current_block.append(lines[index]				)
          index += 1

     # Adds current block if it's nonempty.
     if len(_UpperCAmelCase				) > 0:
          blocks.append('''\n'''.join(_UpperCAmelCase				)				)

     # Add final block after end_prompt if provided.
     if end_prompt is not None and index < len(_UpperCAmelCase				):
          blocks.append('''\n'''.join(lines[index:]				)				)

     return blocks

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Dict:
     '''simple docstring'''


     def _inner(_UpperCAmelCase							:Dict				):
          return key(_UpperCAmelCase				).lower().replace('''_'''  ,						''''''				)

     return _inner

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:Dict=None				)	->				Union[str, Any]:
     '''simple docstring'''


     def noop(_UpperCAmelCase							:int				):
          return x

     if key is None:
          A_          =       noop
     # Constants are all uppercase, they go first.
     A_          =       [obj for obj in objects if key(_UpperCAmelCase				).isupper()]
     # Classes are not all uppercase but start with a capital, they go second.
     A_          =       [obj for obj in objects if key(_UpperCAmelCase				)[0].isupper() and not key(_UpperCAmelCase				).isupper()]
     # Functions begin with a lowercase, they go last.
     A_          =       [obj for obj in objects if not key(_UpperCAmelCase				)[0].isupper()]

     A_          =       ignore_underscore(_UpperCAmelCase				)
     return sorted(_UpperCAmelCase  ,						key=_UpperCAmelCase				) + sorted(_UpperCAmelCase  ,						key=_UpperCAmelCase				) + sorted(_UpperCAmelCase  ,						key=_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:int				)	->				str:
     '''simple docstring'''


     def _replace(_UpperCAmelCase							:List[Any]				):
          A_          =       match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          A_          =       [part.strip().replace('''"'''  ,						''''''				) for part in imports.split(''','''				)]
          # We will have a final empty element if the line finished with a comma.
          if len(keys[-1]				) == 0:
               A_          =       keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(_UpperCAmelCase				)]				) + "]"

     A_          =       import_statement.split('''\n'''				)
     if len(_UpperCAmelCase				) > 3:
          # Here we have to sort internal imports that are on several lines (one per name):
          # key: [
          #     "object1",
          #     "object2",
          #     ...
          # ]

          # We may have to ignore one or two lines on each side.
          A_          =       2 if lines[1].strip() == '''[''' else 1
          A_          =       [(i, _re_strip_line.search(_UpperCAmelCase				).groups()[0]) for i, line in enumerate(lines[idx:-idx]				)]
          A_          =       sort_objects(_UpperCAmelCase  ,						key=lambda _UpperCAmelCase				: x[1]				)
          A_          =       [lines[x[0] + idx] for x in sorted_indices]
          return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]				)
     elif len(_UpperCAmelCase				) == 3:
          # Here we have to sort internal imports that are on one separate line:
          # key: [
          #     "object1", "object2", ...
          # ]
          if _re_bracket_content.search(lines[1]				) is not None:
               A_          =       _re_bracket_content.sub(_replace  ,						lines[1]				)
          else:
               A_          =       [part.strip().replace('''"'''  ,						''''''				) for part in lines[1].split(''','''				)]
               # We will have a final empty element if the line finished with a comma.
               if len(keys[-1]				) == 0:
                    A_          =       keys[:-1]
               A_          =       get_indent(lines[1]				) + ''', '''.join([f'"{k}"' for k in sort_objects(_UpperCAmelCase				)]				)
          return "\n".join(_UpperCAmelCase				)
     else:
          # Finally we have to deal with imports fitting on one line
          A_          =       _re_bracket_content.sub(_replace  ,						_UpperCAmelCase				)
          return import_statement

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:int=True				)	->				str:
     '''simple docstring'''


     with open(_UpperCAmelCase  ,						encoding='''utf-8'''				) as f:
          A_          =       f.read()

     if "_import_structure" not in code:
          return

     # Blocks of indent level 0
     A_          =       split_code_in_indented_blocks(
         _UpperCAmelCase  ,						start_prompt='''_import_structure = {'''  ,						end_prompt='''if TYPE_CHECKING:'''				)

     # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1  ,						len(_UpperCAmelCase				) - 1				):
          # Check if the block contains some `_import_structure`s thingy to sort.
          A_          =       main_blocks[block_idx]
          A_          =       block.split('''\n'''				)

          # Get to the start of the imports.
          A_          =       0
          while line_idx < len(_UpperCAmelCase				) and "_import_structure" not in block_lines[line_idx]:
               # Skip dummy import blocks
               if "import dummy" in block_lines[line_idx]:
                    A_          =       len(_UpperCAmelCase				)
               else:
                    line_idx += 1
          if line_idx >= len(_UpperCAmelCase				):
               continue

          # Ignore beginning and last line: they don't contain anything.
          A_          =       '''\n'''.join(block_lines[line_idx:-1]				)
          A_          =       get_indent(block_lines[1]				)
          # Slit the internal block into blocks of indent level 1.
          A_          =       split_code_in_indented_blocks(_UpperCAmelCase  ,						indent_level=_UpperCAmelCase				)
          # We have two categories of import key: list or _import_structure[key].append/extend
          A_          =       _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
          # Grab the keys, but there is a trap: some lines are empty or just comments.
          A_          =       [(pattern.search(_UpperCAmelCase				).groups()[0] if pattern.search(_UpperCAmelCase				) is not None else None) for b in internal_blocks]
          # We only sort the lines with a key.
          A_          =       [(i, key) for i, key in enumerate(_UpperCAmelCase				) if key is not None]
          A_          =       [x[0] for x in sorted(_UpperCAmelCase  ,						key=lambda _UpperCAmelCase				: x[1]				)]

          # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
          A_          =       0
          A_          =       []
          for i in range(len(_UpperCAmelCase				)				):
               if keys[i] is None:
                    reorderded_blocks.append(internal_blocks[i]				)
               else:
                    A_          =       sort_objects_in_import(internal_blocks[sorted_indices[count]]				)
                    reorderded_blocks.append(_UpperCAmelCase				)
                    count += 1

        # And we put our main block back together with its first and last line.
          A_          =       '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]				)

     if code != "\n".join(_UpperCAmelCase				):
          if check_only:
               return True
          else:
               print(f'Overwriting {file}.'				)
               with open(_UpperCAmelCase  ,						'''w'''  ,						encoding='''utf-8'''				) as f:
                    f.write('''\n'''.join(_UpperCAmelCase				)				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[Any]=True				)	->				Tuple:
     '''simple docstring'''


     A_          =       []
     for root, _, files in os.walk(_UpperCAmelCase				):
          if "__init__.py" in files:
               A_          =       sort_imports(os.path.join(_UpperCAmelCase  ,						'''__init__.py'''				)  ,						check_only=_UpperCAmelCase				)
               if result:
                    A_          =       [os.path.join(_UpperCAmelCase  ,						'''__init__.py'''				)]
     if len(_UpperCAmelCase				) > 0:
          raise ValueError(f'Would overwrite {len(_UpperCAmelCase				)} files, run `make style`.'				)


if __name__ == "__main__":
      a__      :     List[Any]         =		argparse.ArgumentParser()
      parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
      a__      :     List[Any]         =		parser.parse_args()

      sort_imports_in_all_inits(check_only=args.check_only)



