


"""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 __lowercase  (   lowerCamelCase_	:   Optional[int]    ,       lowerCamelCase_	:   int=100    ,       lowerCamelCase_	:   Union[str, Any]=" "	):
				SCREAMING_SNAKE_CASE__  				=					text.split(lowerCamelCase_	)
				return [character.join(text[i : i + n]	).strip() for i in range(0    ,       len(lowerCamelCase_	)    ,       lowerCamelCase_	)]


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


def __lowercase  (   lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   Tuple    ,       lowerCamelCase_	:   Union[str, Any]    ,       lowerCamelCase_	:   Tuple    ,       lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   Optional[int]	):
				SCREAMING_SNAKE_CASE__  				=					load_dataset(
				    "csv"    ,       data_files=[csv_path]    ,       split="train"    ,       delimiter="\t"    ,       column_names=["title", "text"]	)
				SCREAMING_SNAKE_CASE__  				=					kb_dataset.map(
				    lowerCamelCase_    ,       batched=lowerCamelCase_    ,       num_proc=1	)  # if you want you can load already splitted csv.
				SCREAMING_SNAKE_CASE__  				=					[kb_dataset.shard(lowerCamelCase_    ,       lowerCamelCase_    ,       contiguous=lowerCamelCase_	) for i in range(lowerCamelCase_	)]
				SCREAMING_SNAKE_CASE__  				=					kb_list[process_num]

				SCREAMING_SNAKE_CASE__  				=					'''data_''' + str(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					os.path.join(lowerCamelCase_    ,       lowerCamelCase_	)

				SCREAMING_SNAKE_CASE__  				=					DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"	)
				SCREAMING_SNAKE_CASE__  				=					ctx_encoder.to(device=lowerCamelCase_	)

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

				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__  				=					data_shrad.map(
				    partial(lowerCamelCase_    ,       ctx_encoder=lowerCamelCase_    ,       ctx_tokenizer=lowerCamelCase_    ,       device=lowerCamelCase_	)    ,       batched=lowerCamelCase_    ,       batch_size=16    ,       features=lowerCamelCase_    ,       )
				dataset.save_to_disk(lowerCamelCase_	)


def __lowercase  (   lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   Optional[int]	):
				SCREAMING_SNAKE_CASE__  				=					[]

				for shard_address in glob(str(lowerCamelCase_	) + "/*/"	):
								data_shard_list.append(load_from_disk(lowerCamelCase_	)	)

				SCREAMING_SNAKE_CASE__  				=					concatenate_datasets(lowerCamelCase_	)
				faiss.omp_set_num_threads(96	)

				SCREAMING_SNAKE_CASE__  				=					faiss.IndexHNSWFlat(768    ,       128    ,       faiss.METRIC_INNER_PRODUCT	)
				concat.add_faiss_index("embeddings"    ,       custom_index=lowerCamelCase_	)
				concat.get_index("embeddings"	).save(
				    lowerCamelCase_	)  # since we load the index in to memory,we can directly update the index in the disk



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							:  Union[str, Any]     =				logging.get_logger(__name__)

_lowerCamelCase							:  Optional[int]     =				{
    "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    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	"""m2m_100"""
   UpperCamelCase  	=	["""past_key_values"""]
   UpperCamelCase  	=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



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

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






class    __UpperCAmelCase	(			lowerCamelCase__		):



   @property
   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				List[Any]										=					OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
         ]		)

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

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



   def 			__magic_name__       (				self				:	Dict,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	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
     UpperCAmelCase			:				List[str]										=					compute_effective_axis_dimension(
         __A,						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
     UpperCAmelCase			:				Optional[int]										=					tokenizer.num_special_tokens_to_add(__A		)
     UpperCAmelCase			:				int										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_sequence,						num_token_to_add=__A		)

     # Generate dummy inputs according to compute batch and sequence
     UpperCAmelCase			:				List[str]										=					[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
     UpperCAmelCase			:				Tuple										=					dict(tokenizer(__A,						return_tensors=__A		)		)
     return common_inputs



   def 			__magic_name__       (				self				:	Any,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     UpperCAmelCase			:				int										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)

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

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

       UpperCAmelCase			:				Union[str, Any]										=					torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(__A,						__A		)],						dim=1		)

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

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





   UpperCamelCase  	=	_generate_dummy_inputs_for_default_and_seqaseq_lm





