
"""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


_lowercase							      = logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowercase							      = '''cuda''' if torch.cuda.is_available() else '''cpu'''






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   Dict=100		,					snake_case__      :   int=" "   ):
	A        =      text.split(snake_case__   )
	return [character.join(text[i : i + n]   ).strip() for i in range(0		,					len(snake_case__   )		,					snake_case__   )]






def 					_snake_case (						snake_case__      :   dict   ):
	A      ,					A        =      [], []
	for title, text in zip(documents['title']		,					documents['text']   ):
		if text is not None:
			for passage in split_text(snake_case__   ):
				titles.append(title if title is not None else ''   )
				texts.append(snake_case__   )
	return {"title": titles, "text": texts}






def 					_snake_case (						snake_case__      :   dict		,					snake_case__      :   DPRContextEncoder		,					snake_case__      :   DPRContextEncoderTokenizerFast   ):
	A        =      ctx_tokenizer(
	    documents['title']		,					documents['text']		,					truncation=snake_case__		,					padding='longest'		,					return_tensors='pt'   )['input_ids']
	A        =      ctx_encoder(input_ids.to(device=snake_case__   )		,					return_dict=snake_case__   ).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}






def 					_snake_case (						snake_case__      :   "RagExampleArguments"		,					snake_case__      :   "ProcessingArguments"		,					snake_case__      :   "IndexHnswArguments"		,					):
	######################################
	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(snake_case__		,					batched=snake_case__		,					num_proc=processing_args.num_proc   )

	# And compute the embeddings
	A        =      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=snake_case__   )
	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(snake_case__		,					ctx_encoder=snake_case__		,					ctx_tokenizer=snake_case__   )		,					batched=snake_case__		,					batch_size=processing_args.batch_size		,					features=snake_case__		,					)

	# And finally save your dataset
	A        =      os.path.join(rag_example_args.output_dir		,					'my_knowledge_dataset'   )
	dataset.save_to_disk(snake_case__   )
	# 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=snake_case__   )

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







@dataclass
class  lowerCAmelCase_ :



						'''simple docstring'''


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







@dataclass
class  lowerCAmelCase_ :



						'''simple docstring'''


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







@dataclass
class  lowerCAmelCase_ :



						'''simple docstring'''


						_lowerCamelCase:			int					=			field(
						    default=768						,    metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}						,    )
						_lowerCamelCase:			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)

		_lowercase							      = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
		_lowercase      ,			_lowercase      ,			_lowercase							      = parser.parse_args_into_dataclasses()
		with TemporaryDirectory() as tmp_dir:
				_lowercase							      = rag_example_args.output_dir or tmp_dir
				main(rag_example_args, processing_args, index_hnsw_args)

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:Any     ,   a		:List[Any]=1_3     ,   a		:Any=3_0     ,   a		:Dict=2     ,   a		:List[str]=3     ,   a		:Union[str, Any]=True     ,   a		:Optional[int]=True     ,   a		:Tuple=3_2     ,   a		:Any=5     ,   a		:List[str]=4     ,   a		:Optional[Any]=3_7     ,   a		:List[Any]="gelu"     ,   a		:Any=0.1     ,   a		:Any=0.1     ,   a		:List[str]=1_0     ,   a		:str=0.02     ,   )		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					parent
								__UpperCamelCase	:   List[Any]			   =					batch_size
								__UpperCamelCase	:   Optional[Any]			   =					image_size
								__UpperCamelCase	:   int			   =					patch_size
								__UpperCamelCase	:   Optional[Any]			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Tuple			   =					hidden_size
								__UpperCamelCase	:   List[Any]			   =					num_hidden_layers
								__UpperCamelCase	:   List[Any]			   =					num_attention_heads
								__UpperCamelCase	:   Dict			   =					intermediate_size
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   List[Any]			   =					hidden_dropout_prob
								__UpperCamelCase	:   str			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   List[str]			   =					type_sequence_label_size
								__UpperCamelCase	:   List[str]			   =					initializer_range

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Union[str, Any]			   =					(image_size // patch_size) ** 2
								__UpperCamelCase	:   Optional[int]			   =					num_patches + 1







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   Tuple			   =					ViTConfig(
								    image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=a     ,   initializer_range=self.initializer_range     ,   )

								return config, pixel_values







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[int]     ,   a		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTModel(config=a						)
								__UpperCamelCase	:   int			   =					model(a						)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Any			   =					(self.image_size, self.image_size)
								__UpperCamelCase	:   Optional[int]			   =					(self.patch_size, self.patch_size)
								__UpperCamelCase	:   Optional[int]			   =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[str]     ,   a		:Any						)		-> str:
								__UpperCamelCase	:   Dict			   =					self.type_sequence_label_size
								__UpperCamelCase	:   Dict			   =					FlaxViTForImageClassification(config=a						)
								__UpperCamelCase	:   Tuple			   =					model(a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)

								# test greyscale images
								__UpperCamelCase	:   Dict			   =					1
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTForImageClassification(a						)

								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								__UpperCamelCase	:   str			   =					model(a						)







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.prepare_config_and_inputs()
								(
								    (
								    __UpperCamelCase
								)   ,						(
								    __UpperCamelCase
								)   ,						
								)	:   List[Any]			   =					config_and_inputs
								__UpperCamelCase	:   str			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_flax
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> None:
								__UpperCamelCase	:   int			   =					FlaxViTModelTester(self						)
								__UpperCamelCase	:   Any			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a     ,   hidden_size=3_7						)







	def    _lowerCamelCase				(					self		:List[Any]						)		-> str:
								self.config_tester.run_common_tests()







	def    _lowerCamelCase				(					self		:List[Any]						)		-> List[str]:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Any			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class(a						)
															__UpperCamelCase	:   Optional[int]			   =					inspect.signature(model.__call__						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__UpperCamelCase	:   Dict			   =					[*signature.parameters.keys()]

															__UpperCamelCase	:   Any			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																						__UpperCamelCase	:   List[Any]			   =					self._prepare_for_class(a     ,   a						)
																						__UpperCamelCase	:   List[Any]			   =					model_class(a						)

																						@jax.jit
																						def model_jitted(a		:List[str]     ,   **a		:List[Any]						):
																													return model(pixel_values=a     ,   **a						)

																						with self.subTest("JIT Enabled"						):
																													__UpperCamelCase	:   int			   =					model_jitted(**a						).to_tuple()

																						with self.subTest("JIT Disabled"						):
																													with jax.disable_jit():
																																				__UpperCamelCase	:   Optional[Any]			   =					model_jitted(**a						).to_tuple()

																						self.assertEqual(len(a						)     ,   len(a						)						)
																						for jitted_output, output in zip(a     ,   a						):
																													self.assertEqual(jitted_output.shape     ,   output.shape						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> str:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class_name.from_pretrained("google/vit-base-patch16-224"						)
															__UpperCamelCase	:   Optional[int]			   =					model(np.ones((1, 3, 2_2_4, 2_2_4)						)						)
															self.assertIsNotNone(a						)