import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import BlipTextModel
	from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase=7      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase=9_9      ,						UpperCAmelCase=3_2      ,						UpperCAmelCase=3_2      ,						UpperCAmelCase=5      ,						UpperCAmelCase=4      ,						UpperCAmelCase=3_7      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=5_1_2      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=0      ,						UpperCAmelCase=None      ,						) ->    Optional[int]:
								__a							=   parent
								__a							=   batch_size
								__a							=   seq_length
								__a							=   is_training
								__a							=   use_input_mask
								__a							=   use_labels
								__a							=   vocab_size
								__a							=   hidden_size
								__a							=   projection_dim
								__a							=   num_hidden_layers
								__a							=   num_attention_heads
								__a							=   intermediate_size
								__a							=   dropout
								__a							=   attention_dropout
								__a							=   max_position_embeddings
								__a							=   initializer_range
								__a							=   scope
								__a							=   bos_token_id



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size    )

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

								if input_mask is not None:
													__a					,      __a							=   input_mask.shape
													__a							=   np.random.randint(1      ,						seq_length - 1      ,						size=(batch_size,)    )
													for batch_idx, start_index in enumerate(UpperCAmelCase    ):
																		__a							=   1
																		__a							=   0

								__a							=   self.get_config()

								return config, input_ids, input_mask



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return BlipTextConfig(
								    vocab_size=self.vocab_size      ,						hidden_size=self.hidden_size      ,						projection_dim=self.projection_dim      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						dropout=self.dropout      ,						attention_dropout=self.attention_dropout      ,						max_position_embeddings=self.max_position_embeddings      ,						initializer_range=self.initializer_range      ,						bos_token_id=self.bos_token_id      ,						)



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								__a							=   BlipTextModel(config=UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()
								with torch.no_grad():
													__a							=   model(UpperCAmelCase      ,						attention_mask=UpperCAmelCase    )
													__a							=   model(UpperCAmelCase    )
								self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)    )
								self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.hidden_size)    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								__a							=   self.prepare_config_and_inputs()
								__a					,      __a					,      __a							=   config_and_inputs
								__a							=   {'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict






@require_torch
class 						a__							(   __snake_case      ,						unittest.TestCase		):
			A__				:   List[str]								=			(BlipTextModel,) if is_torch_available() else ()
			A__				:   List[Any]								=			False
			A__				:   int								=			False
			A__				:   List[Any]								=			False



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   BlipTextModelTester(self    )
								__a							=   ConfigTester(self      ,						config_class=UpperCAmelCase      ,						hidden_size=3_7    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								self.config_tester.run_common_tests()



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								pass



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								pass



			@unittest.skip(reason='Blip does not use inputs_embeds'    )
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								pass



			@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'    )
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								pass



			@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'    )
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								pass



			@slow
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a							=   BlipTextModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCAmelCase    )





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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					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
					__a							=   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
					__a							=   dataset.map(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

					# And compute the embeddings
					__a							=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowerCamelCase						)
					__a							=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
					__a							=   Features(
					    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
					__a							=   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
					__a							=   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
					__a							=   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
					__a							=   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

					######################################
					logger.info('Step 3 - Load RAG'						)
					######################################

					# Easy way to load the model
					__a							=   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,				index_name='custom'      ,				indexed_dataset=__lowerCamelCase						)
					__a							=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,				retriever=__lowerCamelCase						)
					__a							=   RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

					# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
					# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

					######################################
					logger.info('Step 4 - Have fun'						)
					######################################

					__a							=   rag_example_args.question or 'What does Moses\' rod turn into ?'
					__a							=   tokenizer.question_encoder(__lowerCamelCase      ,				return_tensors='pt'						)['input_ids']
					__a							=   model.generate(__lowerCamelCase						)
					__a							=   tokenizer.batch_decode(__lowerCamelCase      ,				skip_special_tokens=__lowerCamelCase						)[0]
					logger.info('Q: ' + question						)
					logger.info('A: ' + generated_string						)


@dataclass
class 						a__							:
			A__				:   str								=			field(
			    default=str(Path(__snake_case		).parent / 'test_data' / 'my_knowledge_dataset.csv'		)      ,						metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}      ,						)
			A__				:   str								=			field(
			    default='facebook/rag-sequence-nq'      ,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}      ,						)
			A__				:   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\''
			        )
			    }      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}      ,						)






@dataclass
class 						a__							:
			A__				:   Optional[int]								=			field(
			    default=__snake_case      ,						metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }      ,						)
			A__				:   int								=			field(
			    default=16      ,						metadata={
			        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
			    }      ,						)






@dataclass
class 						a__							:
			A__				:   int								=			field(
			    default=7_68      ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,						)
			A__				:   int								=			field(
			    default=1_28      ,						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_				:		Tuple       						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Union[str, Any]       						=       parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCamelCase_				:		Union[str, Any]       						=       rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)




