
import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=100			,				SCREAMING_SNAKE_CASE_=" "	)							->						List[Any]:
			lowerCAmelCase__ : Optional[int]   =						text.split(UpperCAmelCase_	)
			return [character.join(text[i : i + n]	).strip() for i in range(0			,				len(UpperCAmelCase_	)			,				UpperCAmelCase_	)]
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			lowerCAmelCase__ ,							lowerCAmelCase__ : List[Any]   =						[], []
			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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			lowerCAmelCase__ : Optional[Any]   =						load_dataset(
			    'csv'			,				data_files=[csv_path]			,				split='train'			,				delimiter='\t'			,				column_names=['title', 'text']	)
			lowerCAmelCase__ : int   =						kb_dataset.map(
			    UpperCAmelCase_			,				batched=UpperCAmelCase_			,				num_proc=1	)  # if you want you can load already splitted csv.
			lowerCAmelCase__ : str   =						[kb_dataset.shard(UpperCAmelCase_			,				UpperCAmelCase_			,				contiguous=UpperCAmelCase_	) for i in range(UpperCAmelCase_	)]
			lowerCAmelCase__ : Optional[Any]   =						kb_list[process_num]

			lowerCAmelCase__ : Union[str, Any]   =						'data_' + str(UpperCAmelCase_	)
			lowerCAmelCase__ : Union[str, Any]   =						os.path.join(UpperCAmelCase_			,				UpperCAmelCase_	)

			lowerCAmelCase__ : str   =						DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'	)
			lowerCAmelCase__ : Any   =						ctx_encoder.to(device=UpperCAmelCase_	)

			def embed(
			    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	) -> dict:
						lowerCAmelCase__ : List[Any]   =						ctx_tokenizer(
						    documents['title']			,				documents['text']			,				truncation=UpperCAmelCase_			,				padding='longest'			,				return_tensors='pt'	)['input_ids']
						lowerCAmelCase__ : Optional[int]   =						ctx_encoder(input_ids.to(device=UpperCAmelCase_	)			,				return_dict=UpperCAmelCase_	).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}

			lowerCAmelCase__ : List[Any]   =						Features(
			    {'text': Value('string'	), 'title': Value('string'	), 'embeddings': Sequence(Value('float32'	)	)}	)  # optional, save as float32 instead of float64 to save space

			lowerCAmelCase__ : Union[str, Any]   =						data_shrad.map(
			    partial(UpperCAmelCase_			,				ctx_encoder=UpperCAmelCase_			,				ctx_tokenizer=UpperCAmelCase_			,				device=UpperCAmelCase_	)			,				batched=UpperCAmelCase_			,				batch_size=16			,				features=UpperCAmelCase_			,				)
			dataset.save_to_disk(UpperCAmelCase_	)







def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						int:
			lowerCAmelCase__ : Tuple   =						[]

			for shard_address in glob(str(UpperCAmelCase_	) + '/*/'	):
						data_shard_list.append(load_from_disk(UpperCAmelCase_	)	)

			lowerCAmelCase__ : List[str]   =						concatenate_datasets(UpperCAmelCase_	)
			faiss.omp_set_num_threads(96	)

			lowerCAmelCase__ : Union[str, Any]   =						faiss.IndexHNSWFlat(768			,				128			,				faiss.METRIC_INNER_PRODUCT	)
			concat.add_faiss_index('embeddings'			,				custom_index=UpperCAmelCase_	)
			concat.get_index('embeddings'	).save(
			    UpperCAmelCase_	)  # since we load the index in to memory,we can directly update the index in the disk





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_    :       int       =							{
    '''agents''': ['''Agent''', '''AzureOpenAiAgent''', '''HfAgent''', '''LocalAgent''', '''OpenAiAgent'''],
    '''base''': ['''PipelineTool''', '''RemoteTool''', '''Tool''', '''launch_gradio_demo''', '''load_tool'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       int       =							['''DocumentQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''ImageCaptioningTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''ImageQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       str       =							['''ImageSegmentationTool''']
 SCREAMING_SNAKE_CASE_    :       Any       =							['''SpeechToTextTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''TextClassificationTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''TextQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TextSummarizationTool''']
 SCREAMING_SNAKE_CASE_    :       Optional[int]       =							['''TextToSpeechTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TranslationTool''']

if TYPE_CHECKING:
 from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
 from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .document_question_answering import DocumentQuestionAnsweringTool
  from .image_captioning import ImageCaptioningTool
  from .image_question_answering import ImageQuestionAnsweringTool
  from .image_segmentation import ImageSegmentationTool
  from .speech_to_text import SpeechToTextTool
  from .text_classification import TextClassificationTool
  from .text_question_answering import TextQuestionAnsweringTool
  from .text_summarization import TextSummarizationTool
  from .text_to_speech import TextToSpeechTool
  from .translation import TranslationTool
else:
 import sys

 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






