"""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


__lowerCAmelCase     :				List[Any]    				=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCAmelCase     :				Tuple    				=    "cuda" if torch.cuda.is_available() else "cpu"







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__=100      ,	lowerCamelCase__=" "  ):





		"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = [], []
		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 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	):





		"""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
		lowerCAmelCase__   = 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
		lowerCAmelCase__   = dataset.map(lowerCamelCase__      ,	batched=lowerCamelCase__      ,	num_proc=processing_args.num_proc  )

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

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

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

@dataclass
class    a_   :
					UpperCamelCase_			:						str      =     field(
					    default=str(Path(__UpperCamelCase      ).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=__UpperCamelCase			,		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(__UpperCamelCase      ).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=__UpperCamelCase			,		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)

			__lowerCAmelCase     :				List[Any]    				=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
			__lowerCAmelCase				,						__lowerCAmelCase				,						__lowerCAmelCase     :				Any    				=    parser.parse_args_into_dataclasses()
			with TemporaryDirectory() as tmp_dir:
						__lowerCAmelCase     :				List[Any]    				=    rag_example_args.output_dir or tmp_dir
						main(rag_example_args, processing_args, index_hnsw_args)





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.xglm.modeling_tf_xglm import (
			    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFXGLMForCausalLM,
			    TFXGLMModel,
			)

@require_tf
class    a_   :
					UpperCamelCase_			:						Any      =     XGLMConfig
					UpperCamelCase_			:						int      =     {}
					UpperCamelCase_			:						Tuple      =     "gelu"




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Tuple	,    snake_case__    :  List[str]=14	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[Any]=True	,    snake_case__    :  Optional[int]=True	,    snake_case__    :  Union[str, Any]=True	,    snake_case__    :  Tuple=99	,    snake_case__    :  Optional[Any]=32	,    snake_case__    :  List[Any]=2	,    snake_case__    :  Optional[Any]=4	,    snake_case__    :  List[str]=37	,    snake_case__    :  Optional[Any]="gelu"	,    snake_case__    :  str=0.1	,    snake_case__    :  List[Any]=0.1	,    snake_case__    :  List[Any]=512	,    snake_case__    :  List[Any]=0.02	,    ):
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = seq_length
							lowerCAmelCase__   = is_training
							lowerCAmelCase__   = use_input_mask
							lowerCAmelCase__   = use_labels
							lowerCAmelCase__   = vocab_size
							lowerCAmelCase__   = d_model
							lowerCAmelCase__   = num_hidden_layers
							lowerCAmelCase__   = num_attention_heads
							lowerCAmelCase__   = ffn_dim
							lowerCAmelCase__   = activation_function
							lowerCAmelCase__   = activation_dropout
							lowerCAmelCase__   = attention_dropout
							lowerCAmelCase__   = max_position_embeddings
							lowerCAmelCase__   = initializer_range
							lowerCAmelCase__   = None
							lowerCAmelCase__   = 0
							lowerCAmelCase__   = 2
							lowerCAmelCase__   = 1




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							return XGLMConfig.from_pretrained("""facebook/xglm-564M"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )	,    clip_value_min=0	,    clip_value_max=3  )

							lowerCAmelCase__   = None
							if self.use_input_mask:
									lowerCAmelCase__   = random_attention_mask([self.batch_size, self.seq_length]  )

							lowerCAmelCase__   = self.get_config()

							lowerCAmelCase__   = floats_tensor([self.num_hidden_layers, self.num_attention_heads]	,    2  )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							return XGLMConfig(
							    vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    num_layers=self.num_hidden_layers	,    attention_heads=self.num_attention_heads	,    ffn_dim=self.ffn_dim	,    activation_function=self.activation_function	,    activation_dropout=self.activation_dropout	,    attention_dropout=self.attention_dropout	,    max_position_embeddings=self.max_position_embeddings	,    initializer_range=self.initializer_range	,    use_cache=snake_case__	,    bos_token_id=self.bos_token_id	,    eos_token_id=self.eos_token_id	,    pad_token_id=self.pad_token_id	,    return_dict=snake_case__	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = self.prepare_config_and_inputs()

							(
							    (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       
							)   = config_and_inputs

							lowerCAmelCase__   = {
							    """input_ids""": input_ids,
							    """head_mask""": head_mask,
							}

							return config, inputs_dict

@require_tf
class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						str      =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					UpperCamelCase_			:						Union[str, Any]      =     (TFXGLMForCausalLM,) if is_tf_available() else ()
					UpperCamelCase_			:						Tuple      =     (
					    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
					)
					UpperCamelCase_			:						List[Any]      =     False
					UpperCamelCase_			:						Optional[Any]      =     False
					UpperCamelCase_			:						str      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = TFXGLMModelTester(self  )
							lowerCAmelCase__   = ConfigTester(self	,    config_class=snake_case__	,    n_embd=37  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							self.config_tester.run_common_tests()




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__   = TFXGLMModel.from_pretrained(snake_case__  )
									self.assertIsNotNone(snake_case__  )




					@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							super().test_resize_token_embeddings()

@require_tf
class    a_   (  unittest.TestCase      ):




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]=True  ):
							lowerCAmelCase__   = TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
							lowerCAmelCase__   = tf.convert_to_tensor([[2, 268, 9865]]	,    dtype=tf.intaa  )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							lowerCAmelCase__   = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
							# fmt: on
							lowerCAmelCase__   = model.generate(snake_case__	,    do_sample=snake_case__	,    num_beams=1  )
							if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()	,    snake_case__  )




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )
							lowerCAmelCase__   = TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )

							tf.random.set_seed(0  )
							lowerCAmelCase__   = tokenizer("""Today is a nice day and"""	,    return_tensors="""tf"""  )
							lowerCAmelCase__   = tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(""":/CPU:0"""  ):
									lowerCAmelCase__   = model.generate(snake_case__	,    do_sample=snake_case__	,    seed=[7, 0]  )
							lowerCAmelCase__   = tokenizer.decode(output_ids[0]	,    skip_special_tokens=snake_case__  )

							lowerCAmelCase__   = (
							    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
							)
							self.assertEqual(snake_case__	,    snake_case__  )




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
							lowerCAmelCase__   = XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )

							lowerCAmelCase__   = """left"""

							# use different length sentences to test batching
							lowerCAmelCase__   = [
							    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
							    """left-padding, such as in batched generation. The output for the sequence below should be the same """
							    """regardless of whether left padding is applied or not. When""",
							    """Hello, my dog is a little""",
							]

							lowerCAmelCase__   = tokenizer(snake_case__	,    return_tensors="""tf"""	,    padding=snake_case__  )
							lowerCAmelCase__   = inputs["""input_ids"""]

							lowerCAmelCase__   = model.generate(input_ids=snake_case__	,    attention_mask=inputs["""attention_mask"""]	,    max_new_tokens=12  )

							lowerCAmelCase__   = tokenizer(sentences[0]	,    return_tensors="""tf"""  ).input_ids
							lowerCAmelCase__   = model.generate(input_ids=snake_case__	,    max_new_tokens=12  )

							lowerCAmelCase__   = tokenizer(sentences[1]	,    return_tensors="""tf"""  ).input_ids
							lowerCAmelCase__   = model.generate(input_ids=snake_case__	,    max_new_tokens=12  )

							lowerCAmelCase__   = tokenizer.batch_decode(snake_case__	,    skip_special_tokens=snake_case__  )
							lowerCAmelCase__   = tokenizer.decode(output_non_padded[0]	,    skip_special_tokens=snake_case__  )
							lowerCAmelCase__   = tokenizer.decode(output_padded[0]	,    skip_special_tokens=snake_case__  )

							lowerCAmelCase__   = [
							    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
							    """left-padding, such as in batched generation. The output for the sequence below should be the same """
							    """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
							    """a single""",
							    """Hello, my dog is a little bit of a shy one, but he is very friendly""",
							]
							self.assertListEqual(snake_case__	,    snake_case__  )
							self.assertListEqual(snake_case__	,    [non_padded_sentence, padded_sentence]  )




