





"""simple docstring"""





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_	(  lowercase_   :							Optional[int] ,      lowercase_   :							int=100 ,      lowercase_   :							int=" "		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any]		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							int ,      lowercase_   :							Tuple ,      lowercase_   :							Tuple ,      lowercase_   :							Optional[int] ,      lowercase_   :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       load_dataset(
     '''csv''' ,      data_files=[csv_path] ,      split='''train''' ,      delimiter='''\t''' ,      column_names=['''title''', '''text''']		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       kb_dataset.map(
     lowercase_ ,      batched=lowercase_ ,      num_proc=1		)  # if you want you can load already splitted csv.
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       [kb_dataset.shard(lowercase_ ,      lowercase_ ,      contiguous=lowercase_		) for i in range(lowercase_		)]
 __SCREAMING_SNAKE_CASE :			Any												=       kb_list[process_num]

 __SCREAMING_SNAKE_CASE :			Dict												=       '''data_''' + str(lowercase_		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       os.path.join(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			int												=       DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       ctx_encoder.to(device=lowercase_		)

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

 __SCREAMING_SNAKE_CASE :			Dict												=       Features(
     {'''text''': Value('''string'''		), '''title''': Value('''string'''		), '''embeddings''': Sequence(Value('''float32'''		)		)}		)  # optional, save as float32 instead of float64 to save space

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






def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []

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

 __SCREAMING_SNAKE_CASE :			List[Any]												=       concatenate_datasets(lowercase_		)
 faiss.omp_set_num_threads(96		)

 __SCREAMING_SNAKE_CASE :			int												=       faiss.IndexHNSWFlat(768 ,      128 ,      faiss.METRIC_INNER_PRODUCT		)
 concat.add_faiss_index('''embeddings''' ,      custom_index=lowercase_		)
 concat.get_index('''embeddings'''		).save(
     lowercase_		)  # since we load the index in to memory,we can directly update the index in the disk








"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= '''m2m_100'''
      lowerCamelCase__						= ['''past_key_values''']
      lowerCamelCase__						= {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

      def __init__(			self   :int						,							_lowerCamelCase   :Union[str, Any]=1_2_8_1_1_2						,							_lowerCamelCase   :str=1_0_2_4						,							_lowerCamelCase   :Any=1_2						,							_lowerCamelCase   :Dict=4_0_9_6						,							_lowerCamelCase   :List[str]=1_6						,							_lowerCamelCase   :Union[str, Any]=1_2						,							_lowerCamelCase   :Any=4_0_9_6						,							_lowerCamelCase   :str=1_6						,							_lowerCamelCase   :Union[str, Any]=0.0_5						,							_lowerCamelCase   :Any=0.0_5						,							_lowerCamelCase   :int=True						,							_lowerCamelCase   :Dict=True						,							_lowerCamelCase   :List[str]="relu"						,							_lowerCamelCase   :List[Any]=1_0_2_4						,							_lowerCamelCase   :Dict=0.1						,							_lowerCamelCase   :List[Any]=0.1						,							_lowerCamelCase   :Any=0.0						,							_lowerCamelCase   :Tuple=0.0_2						,							_lowerCamelCase   :Dict=2						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :Any=1						,							_lowerCamelCase   :Dict=0						,							_lowerCamelCase   :List[Any]=2						,							**_lowerCamelCase   :Optional[Any]						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Any												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			int												=       d_model
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			int												=       encoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Dict												=       decoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layers
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       decoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dropout
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			List[str]												=       activation_dropout
       __SCREAMING_SNAKE_CASE :			List[Any]												=       activation_function
       __SCREAMING_SNAKE_CASE :			Any												=       init_std
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_cache
       __SCREAMING_SNAKE_CASE :			List[str]												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			str												=       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_lowerCamelCase						,							bos_token_id=_lowerCamelCase						,							eos_token_id=_lowerCamelCase						,							is_encoder_decoder=_lowerCamelCase						,							decoder_start_token_id=_lowerCamelCase						,							**_lowerCamelCase						,							)







class 			snake_case		(   __UpperCAmelCase   ):

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			str												=       OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]						)

       if self.use_past:
        __SCREAMING_SNAKE_CASE :			Dict												=       {0: '''batch'''}
        __SCREAMING_SNAKE_CASE :			Any												=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        __SCREAMING_SNAKE_CASE :			int												=       {0: '''batch''', 1: '''decoder_sequence'''}
        __SCREAMING_SNAKE_CASE :			List[Any]												=       {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
        self.fill_with_past_key_values_(_lowerCamelCase						,							direction='''inputs'''						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       __SCREAMING_SNAKE_CASE :			List[Any]												=       compute_effective_axis_dimension(
           _lowerCamelCase						,							fixed_dimension=OnnxConfig.default_fixed_batch						,							num_token_to_add=0						)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.num_special_tokens_to_add(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       compute_effective_axis_dimension(
           _lowerCamelCase						,							fixed_dimension=OnnxConfig.default_fixed_sequence						,							num_token_to_add=_lowerCamelCase						)

       # Generate dummy inputs according to compute batch and sequence
       __SCREAMING_SNAKE_CASE :			int												=       [''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       dict(tokenizer(_lowerCamelCase						,							return_tensors=_lowerCamelCase						)						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       __SCREAMING_SNAKE_CASE :			Dict												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       # Generate decoder inputs
       __SCREAMING_SNAKE_CASE :			Dict												=       seq_length if not self.use_past else 1
       __SCREAMING_SNAKE_CASE :			Any												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dict(**_lowerCamelCase						,							**_lowerCamelCase						)

       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
        else:
         import torch
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       common_inputs['''input_ids'''].shape
        __SCREAMING_SNAKE_CASE :			Tuple												=       common_inputs['''decoder_input_ids'''].shape[1]
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.num_attention_heads
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_seq_length + 3
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __SCREAMING_SNAKE_CASE :			Any												=       torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase						,							_lowerCamelCase						)]						,							dim=1						)

        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.num_layers
        __SCREAMING_SNAKE_CASE :			Any												=       min(_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       max(_lowerCamelCase						,							_lowerCamelCase						) - min_num_layers
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

        for _ in range(_lowerCamelCase						):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
             )						)
        # TODO: test this.
        __SCREAMING_SNAKE_CASE :			Any												=       encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
        for _ in range(_lowerCamelCase						,							_lowerCamelCase						):
         common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase						), torch.zeros(_lowerCamelCase						))						)
       return common_inputs



      lowerCamelCase__						= _generate_dummy_inputs_for_default_and_seqaseq_lm

