


import collections
import importlib.util
import os
import re
from pathlib import Path


lowerCamelCase__							:						str							     =   """src/transformers"""


# Matches is_xxx_available()
lowerCamelCase__							:						List[str]							     =   re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase__							:						Any							     =   re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase__							:						Optional[Any]							     =   re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowerCamelCase__							:						Dict							     =   re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase__							:						Tuple							     =   re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase__							:						Dict							     =   re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase__							:						List[Any]							     =   re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase__							:						Dict							     =   re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowerCamelCase__							:						List[Any]							     =   re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowerCamelCase__							:						Dict							     =   re.compile(r"""^\s*try:""")
# Catches a line with else:
lowerCamelCase__							:						Dict							     =   re.compile(r"""^\s*else:""")







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				str:
  if _re_test_backend.search(__lowerCAmelCase				) is None:
    return None
  snake_case__						= [b[0] for b in _re_backend.findall(__lowerCAmelCase				)]
  backends.sort()
  return "_and_".join(__lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  with open(__lowerCAmelCase							,   '''r'''							,   encoding='''utf-8'''							,   newline='''\n'''				) as f:
    snake_case__						= f.readlines()

  snake_case__						= 0
  while line_index < len(__lowerCAmelCase				) and not lines[line_index].startswith('''_import_structure = {'''				):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(__lowerCAmelCase				):
    return None

  # First grab the objects without a specific backend in _import_structure
  snake_case__						= []
  while not lines[line_index].startswith('''if TYPE_CHECKING'''				) and find_backend(lines[line_index]				) is None:
    snake_case__						= lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(__lowerCAmelCase				):
      snake_case__						= _re_one_line_import_struct.search(__lowerCAmelCase				).groups()[0]
      snake_case__						= re.findall('''\[([^\]]+)\]'''							,   __lowerCAmelCase				)
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(''', '''				)]				)
      line_index += 1
      continue
    snake_case__						= _re_import_struct_key_value.search(__lowerCAmelCase				)
    if single_line_import_search is not None:
      snake_case__						= [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''				) if len(__lowerCAmelCase				) > 0]
      objects.extend(__lowerCAmelCase				)
    elif line.startswith(''' ''' * 8 + '''"'''				):
      objects.append(line[9:-3]				)
    line_index += 1

  snake_case__						= {'''none''': objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith('''if TYPE_CHECKING'''				):
    # If the line is an if not is_backend_available, we grab all objects associated.
    snake_case__						= find_backend(lines[line_index]				)
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]				) is None:
      snake_case__						= None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]				) is None:
        line_index += 1

      line_index += 1

      snake_case__						= []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]				) <= 1 or lines[line_index].startswith(''' ''' * 4				):
        snake_case__						= lines[line_index]
        if _re_import_struct_add_one.search(__lowerCAmelCase				) is not None:
          objects.append(_re_import_struct_add_one.search(__lowerCAmelCase				).groups()[0]				)
        elif _re_import_struct_add_many.search(__lowerCAmelCase				) is not None:
          snake_case__						= _re_import_struct_add_many.search(__lowerCAmelCase				).groups()[0].split(''', '''				)
          snake_case__						= [obj[1:-1] for obj in imports if len(__lowerCAmelCase				) > 0]
          objects.extend(__lowerCAmelCase				)
        elif _re_between_brackets.search(__lowerCAmelCase				) is not None:
          snake_case__						= _re_between_brackets.search(__lowerCAmelCase				).groups()[0].split(''', '''				)
          snake_case__						= [obj[1:-1] for obj in imports if len(__lowerCAmelCase				) > 0]
          objects.extend(__lowerCAmelCase				)
        elif _re_quote_object.search(__lowerCAmelCase				) is not None:
          objects.append(_re_quote_object.search(__lowerCAmelCase				).groups()[0]				)
        elif line.startswith(''' ''' * 8 + '''"'''				):
          objects.append(line[9:-3]				)
        elif line.startswith(''' ''' * 12 + '''"'''				):
          objects.append(line[13:-3]				)
        line_index += 1

      snake_case__						= objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  snake_case__						= []
  while (
      line_index < len(__lowerCAmelCase				)
      and find_backend(lines[line_index]				) is None
      and not lines[line_index].startswith('''else'''				)
  ):
    snake_case__						= lines[line_index]
    snake_case__						= _re_import.search(__lowerCAmelCase				)
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(''', '''				)				)
    elif line.startswith(''' ''' * 8				):
      objects.append(line[8:-2]				)
    line_index += 1

  snake_case__						= {'''none''': objects}
  # Let's continue with backend-specific objects
  while line_index < len(__lowerCAmelCase				):
    # If the line is an if is_backend_available, we grab all objects associated.
    snake_case__						= find_backend(lines[line_index]				)
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]				) is None:
      snake_case__						= None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]				) is None:
        line_index += 1

      line_index += 1

      snake_case__						= []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]				) <= 1 or lines[line_index].startswith(''' ''' * 8				):
        snake_case__						= lines[line_index]
        snake_case__						= _re_import.search(__lowerCAmelCase				)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(''', '''				)				)
        elif line.startswith(''' ''' * 12				):
          objects.append(line[12:-2]				)
        line_index += 1

      snake_case__						= objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:

  def find_duplicates(__lowerCAmelCase				):
    return [k for k, v in collections.Counter(__lowerCAmelCase				).items() if v > 1]

  if list(import_dict_objects.keys()				) != list(type_hint_objects.keys()				):
    return ["Both sides of the init do not have the same backends!"]

  snake_case__						= []
  for key in import_dict_objects.keys():
    snake_case__						= find_duplicates(import_dict_objects[key]				)
    if duplicate_imports:
      errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""				)
    snake_case__						= find_duplicates(type_hint_objects[key]				)
    if duplicate_type_hints:
      errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""				)

    if sorted(set(import_dict_objects[key]				)				) != sorted(set(type_hint_objects[key]				)				):
      snake_case__						= '''base imports''' if key == '''none''' else F"""{key} backend"""
      errors.append(F"""Differences for {name}:"""				)
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""				)
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""				)
  return errors







def SCREAMING_SNAKE_CASE	(			)    ->				str:
  snake_case__						= []
  for root, _, files in os.walk(__lowerCAmelCase				):
    if "__init__.py" in files:
      snake_case__						= os.path.join(__lowerCAmelCase							,   '''__init__.py'''				)
      snake_case__						= parse_init(__lowerCAmelCase				)
      if objects is not None:
        snake_case__						= analyze_results(*__lowerCAmelCase				)
        if len(__lowerCAmelCase				) > 0:
          snake_case__						= F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
          failures.append('''\n'''.join(__lowerCAmelCase				)				)
  if len(__lowerCAmelCase				) > 0:
    raise ValueError('''\n\n'''.join(__lowerCAmelCase				)				)







def SCREAMING_SNAKE_CASE	(			)    ->				Tuple:
  snake_case__						= []
  for path, directories, files in os.walk(__lowerCAmelCase				):
    for folder in directories:
      # Ignore private modules
      if folder.startswith('''_'''				):
        directories.remove(__lowerCAmelCase				)
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(__lowerCAmelCase				) / folder).glob('''*.py'''				)				)				) == 0:
        continue
      snake_case__						= str((Path(__lowerCAmelCase				) / folder).relative_to(__lowerCAmelCase				)				)
      snake_case__						= short_path.replace(os.path.sep							,   '''.'''				)
      submodules.append(__lowerCAmelCase				)
    for fname in files:
      if fname == "__init__.py":
        continue
      snake_case__						= str((Path(__lowerCAmelCase				) / fname).relative_to(__lowerCAmelCase				)				)
      snake_case__						= short_path.replace('''.py'''							,   ''''''				).replace(os.path.sep							,   '''.'''				)
      if len(submodule.split('''.'''				)				) == 1:
        submodules.append(__lowerCAmelCase				)
  return submodules


lowerCamelCase__							:						Optional[int]							     =   [
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
]







def SCREAMING_SNAKE_CASE	(			)    ->				Optional[Any]:
  # This is to make sure the transformers module imported is the one in the repo.
  snake_case__						= importlib.util.spec_from_file_location(
      '''transformers'''							,   os.path.join(__lowerCAmelCase							,   '''__init__.py'''				)							,   submodule_search_locations=[PATH_TO_TRANSFORMERS]							,   )
  snake_case__						= spec.loader.load_module()

  snake_case__						= [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(__lowerCAmelCase				) > 0:
    snake_case__						= '''\n'''.join(F"""- {module}""" for module in module_not_registered				)
    raise ValueError(
        '''The following submodules are not properly registered in the main init of Transformers:\n'''
        F"""{list_of_modules}\n"""
        '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''				)


if __name__ == "__main__":
   check_all_inits()
   check_submodules()








'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


a   	=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
a   	=    "cuda" if torch.cuda.is_available() else "cpu"

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							text.split(__UpperCAmelCase   )
   return [character.join(text[i : i + n]   ).strip() for i in range(0				,						len(__UpperCAmelCase   )				,						__UpperCAmelCase   )]

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   for title, text in zip(documents["""title"""]				,						documents["""text"""]   ):
      if text is not None:
         for passage in split_text(__UpperCAmelCase   ):
            titles.append(title if title is not None else """"""   )
            texts.append(__UpperCAmelCase   )
   return {"title": titles, "text": texts}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ctx_tokenizer(
       documents["""title"""]				,						documents["""text"""]				,						truncation=__UpperCAmelCase				,						padding="""longest"""				,						return_tensors="""pt"""   )["""input_ids"""]
   __SCREAMING_SNAKE_CASE   				=							ctx_encoder(input_ids.to(device=__UpperCAmelCase   )				,						return_dict=__UpperCAmelCase   ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''simple docstring'''







   logger.info("""Step 1 - Create the dataset"""   )
   ######################################

   # The dataset needed for RAG must have three columns:
   # - title (string): title of the document
   # - text (string): text of a passage of the document
   # - embeddings (array of dimension d): DPR representation of the passage
   # Let's say you have documents in tab-separated csv files with columns "title" and "text"
   assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

   # You can load a Dataset object this way
   __SCREAMING_SNAKE_CASE   				=							load_dataset(
       """csv"""				,						data_files=[rag_example_args.csv_path]				,						split="""train"""				,						delimiter="""\t"""				,						column_names=["""title""", """text"""]   )

   # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

   # Then split the documents into passages of 100 words
   __SCREAMING_SNAKE_CASE   				=							dataset.map(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						num_proc=processing_args.num_proc   )

   # And compute the embeddings
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
   __SCREAMING_SNAKE_CASE   				=							Features(
       {"""text""": Value("""string"""   ), """title""": Value("""string"""   ), """embeddings""": Sequence(Value("""float32"""   )   )}   )  # optional, save as float32 instead of float64 to save space
   __SCREAMING_SNAKE_CASE   				=							dataset.map(
       partial(__UpperCAmelCase				,						ctx_encoder=__UpperCAmelCase				,						ctx_tokenizer=__UpperCAmelCase   )				,						batched=__UpperCAmelCase				,						batch_size=processing_args.batch_size				,						features=__UpperCAmelCase				,						)

   # And finally save your dataset
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset"""   )
   dataset.save_to_disk(__UpperCAmelCase   )
   # from datasets import load_from_disk
   # dataset = load_from_disk(passages_path)  # to reload the dataset

   ######################################
   logger.info("""Step 2 - Index the dataset"""   )
   ######################################

   # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
   __SCREAMING_SNAKE_CASE   				=							faiss.IndexHNSWFlat(index_hnsw_args.d				,						index_hnsw_args.m				,						faiss.METRIC_INNER_PRODUCT   )
   dataset.add_faiss_index("""embeddings"""				,						custom_index=__UpperCAmelCase   )

   # And save the index
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset_hnsw_index.faiss"""   )
   dataset.get_index("""embeddings"""   ).save(__UpperCAmelCase   )
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      ), metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/rag-sequence-nq', metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/dpr-ctx_encoder-multiset-base', metadata={
           'help': (
               'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
               ' \'facebook/dpr-ctx_encoder-multiset-base\''
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb'      ), metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}, )







@dataclass
class __a			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						field(
       default=768, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}, )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={
           'help': (
               'The number of bi-directional links created for every new element during the HNSW index construction.'
           )
       }, )


if __name__ == "__main__":
    logging.basicConfig(level=logging.WARNING)
    logger.setLevel(logging.INFO)

    a   	=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    a	, a	, a   	=    parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        a   	=    rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)





