
'''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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=1_0_0,		__SCREAMING_SNAKE_CASE=" "       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		):
    """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
    snake_case_ :    List[Any]     			=						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
    snake_case_ :    Optional[int]     			=						dataset.map(__SCREAMING_SNAKE_CASE,		batched=__SCREAMING_SNAKE_CASE,		num_proc=processing_args.num_proc       )

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

    # And finally save your dataset
    snake_case_ :    str     			=						os.path.join(rag_example_args.output_dir,		"my_knowledge_dataset"       )
    dataset.save_to_disk(__SCREAMING_SNAKE_CASE       )
    # 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
    snake_case_ :    str     			=						faiss.IndexHNSWFlat(index_hnsw_args.d,		index_hnsw_args.m,		faiss.METRIC_INNER_PRODUCT       )
    dataset.add_faiss_index("embeddings",		custom_index=__SCREAMING_SNAKE_CASE       )

    # And save the index
    snake_case_ :    Optional[Any]     			=						os.path.join(rag_example_args.output_dir,		"my_knowledge_dataset_hnsw_index.faiss"       )
    dataset.get_index("embeddings"       ).save(__SCREAMING_SNAKE_CASE       )
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						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_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}    ,						)
 UpperCAmelCase_		    =						field(
     default="""facebook/rag-sequence-nq"""    ,						metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}    ,						)
 UpperCAmelCase_		    =						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_		    =						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    UpperCAmelCase_				:
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={
         """help""": """The number of processes to use to split the documents into passages. Default is single process."""
     }    ,						)
 UpperCAmelCase_		    =						field(
     default=1_6    ,						metadata={
         """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
     }    ,						)






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						field(
     default=7_6_8    ,						metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}    ,						)
 UpperCAmelCase_		    =						field(
     default=1_2_8    ,						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)






'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_											=						"src/diffusers"
a_											=						"."


# This is to make sure the diffusers module imported is the one in the repo.
a_											=						importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a_											=						spec.loader.load_module()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    return line.startswith(__SCREAMING_SNAKE_CASE       ) or len(__SCREAMING_SNAKE_CASE       ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$",		__SCREAMING_SNAKE_CASE       ) is not None

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						object_name.split("."       )
    snake_case_ :    Dict     			=						0

    # First let's find the module where our object lives.
    snake_case_ :    int     			=						parts[i]
    while i < len(__SCREAMING_SNAKE_CASE       ) and not os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,		f'{module}.py'       )       ):
        i += 1
        if i < len(__SCREAMING_SNAKE_CASE       ):
            snake_case_ :    List[Any]     			=						os.path.join(__SCREAMING_SNAKE_CASE,		parts[i]       )
    if i >= len(__SCREAMING_SNAKE_CASE       ):
        raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'       )

    with open(os.path.join(__SCREAMING_SNAKE_CASE,		f'{module}.py'       ),		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    str     			=						f.readlines()

    # Now let's find the class / func in the code!
    snake_case_ :    Any     			=						""
    snake_case_ :    Optional[Any]     			=						0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__SCREAMING_SNAKE_CASE       ) and re.search(rf'^{indent}(class|def)\s+{name}(\(|\:)',		lines[line_index]       ) is None
        ):
            line_index += 1
        indent += "    "
        line_index += 1

    if line_index >= len(__SCREAMING_SNAKE_CASE       ):
        raise ValueError(f' {object_name} does not match any function or class in {module}.'       )

    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
    snake_case_ :    Union[str, Any]     			=						line_index
    while line_index < len(__SCREAMING_SNAKE_CASE       ) and _should_continue(lines[line_index],		__SCREAMING_SNAKE_CASE       ):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]       ) <= 1:
        line_index -= 1

    snake_case_ :    Dict     			=						lines[start_index:line_index]
    return "".join(__SCREAMING_SNAKE_CASE       )


a_											=						re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a_											=						re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
a_											=						re.compile(R"<FILL\s+[^>]*>")

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						code.split("\n"       )
    snake_case_ :    Union[str, Any]     			=						0
    while idx < len(__SCREAMING_SNAKE_CASE       ) and len(lines[idx]       ) == 0:
        idx += 1
    if idx < len(__SCREAMING_SNAKE_CASE       ):
        return re.search(r"^(\s*)\S",		lines[idx]       ).groups()[0]
    return ""

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						len(get_indent(__SCREAMING_SNAKE_CASE       )       ) > 0
    if has_indent:
        snake_case_ :    Optional[Any]     			=						f'class Bla:\n{code}'
    snake_case_ :    List[Any]     			=						black.Mode(target_versions={black.TargetVersion.PYaa},		line_length=1_1_9,		preview=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[str]     			=						black.format_str(__SCREAMING_SNAKE_CASE,		mode=__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    Optional[Any]     			=						style_docstrings_in_code(__SCREAMING_SNAKE_CASE       )
    return result[len("class Bla:\n"       ) :] if has_indent else result

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    List[Any]     			=						f.readlines()
    snake_case_ :    str     			=						[]
    snake_case_ :    Tuple     			=						0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Union[str, Any]     			=						_re_copy_warning.search(lines[line_index]       )
        if search is None:
            line_index += 1
            continue

        # There is some copied code here, let's retrieve the original.
        snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						search.groups()
        snake_case_ :    int     			=						find_code_in_diffusers(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    List[Any]     			=						get_indent(__SCREAMING_SNAKE_CASE       )

        snake_case_ :    Dict     			=						line_index + 1 if indent == theoretical_indent else line_index + 2
        snake_case_ :    Dict     			=						theoretical_indent
        snake_case_ :    Tuple     			=						start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        snake_case_ :    str     			=						True
        while line_index < len(__SCREAMING_SNAKE_CASE       ) and should_continue:
            line_index += 1
            if line_index >= len(__SCREAMING_SNAKE_CASE       ):
                break
            snake_case_ :    Dict     			=						lines[line_index]
            snake_case_ :    List[Any]     			=						_should_continue(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ) and re.search(f'^{indent}# End copy',		__SCREAMING_SNAKE_CASE       ) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]       ) <= 1:
            line_index -= 1

        snake_case_ :    Optional[int]     			=						lines[start_index:line_index]
        snake_case_ :    Optional[Any]     			=						"".join(__SCREAMING_SNAKE_CASE       )

        # Remove any nested `Copied from` comments to avoid circular copies
        snake_case_ :    int     			=						[line for line in theoretical_code.split("\n"       ) if _re_copy_warning.search(__SCREAMING_SNAKE_CASE       ) is None]
        snake_case_ :    Dict     			=						"\n".join(__SCREAMING_SNAKE_CASE       )

        # Before comparing, use the `replace_pattern` on the original code.
        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Union[str, Any]     			=						replace_pattern.replace("with",		""       ).split(","       )
            snake_case_ :    Optional[Any]     			=						[_re_replace_pattern.search(__SCREAMING_SNAKE_CASE       ) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                snake_case_			, snake_case_			, snake_case_ :    Optional[Any]     			=						pattern.groups()
                snake_case_ :    Optional[int]     			=						re.sub(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                if option.strip() == "all-casing":
                    snake_case_ :    List[Any]     			=						re.sub(obja.lower(),		obja.lower(),		__SCREAMING_SNAKE_CASE       )
                    snake_case_ :    Optional[int]     			=						re.sub(obja.upper(),		obja.upper(),		__SCREAMING_SNAKE_CASE       )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            snake_case_ :    Optional[int]     			=						blackify(lines[start_index - 1] + theoretical_code       )
            snake_case_ :    Optional[Any]     			=						theoretical_code[len(lines[start_index - 1]       ) :]

        # Test for a diff and act accordingly.
        if observed_code != theoretical_code:
            diffs.append([object_name, start_index]       )
            if overwrite:
                snake_case_ :    List[str]     			=						lines[:start_index] + [theoretical_code] + lines[line_index:]
                snake_case_ :    List[str]     			=						start_index + 1

    if overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        # Warn the user a file has been modified.
        print(f'Detected changes, rewriting {filename}.'       )
        with open(__SCREAMING_SNAKE_CASE,		"w",		encoding="utf-8",		newline="\n"       ) as f:
            f.writelines(__SCREAMING_SNAKE_CASE       )
    return diffs

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE = False       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						glob.glob(os.path.join(__SCREAMING_SNAKE_CASE,		"**/*.py"       ),		recursive=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						[]
    for filename in all_files:
        snake_case_ :    Tuple     			=						is_copy_consistent(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        snake_case_ :    str     			=						"\n".join(__SCREAMING_SNAKE_CASE       )
        raise Exception(
            "Found the following copy inconsistencies:\n"
            + diff
            + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  a_											=						parser.parse_args()

  check_copies(args.fix_and_overwrite)




