

'''simple docstring'''





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int=3		,					UpperCAmelCase__     :		Optional[int]=32		,					UpperCAmelCase__     :		List[str]=3		,					UpperCAmelCase__     :		Tuple=10		,					UpperCAmelCase__     :		List[str]=[10, 20, 30, 40]		,					UpperCAmelCase__     :		Any=[1, 1, 2, 1]		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		List[Any]="relu"		,					UpperCAmelCase__     :		Optional[Any]=3		,					UpperCAmelCase__     :		Tuple=None		,					):
										'''simple docstring'''
										lowercase :							List[Any]    		=parent
										lowercase :							str    		=batch_size
										lowercase :							Optional[int]    		=image_size
										lowercase :							List[Any]    		=num_channels
										lowercase :							Tuple    		=embeddings_size
										lowercase :							int    		=hidden_sizes
										lowercase :							List[str]    		=depths
										lowercase :							Any    		=is_training
										lowercase :							List[Any]    		=use_labels
										lowercase :							Tuple    		=hidden_act
										lowercase :							Dict    		=num_labels
										lowercase :							List[str]    		=scope
										lowercase :							Dict    		=len(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Any    		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										lowercase :							int    		=self.get_config()

										return config, pixel_values







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return RegNetConfig(
										    num_channels=self.num_channels		,					embeddings_size=self.embeddings_size		,					hidden_sizes=self.hidden_sizes		,					depths=self.depths		,					hidden_act=self.hidden_act		,					num_labels=self.num_labels		,					image_size=self.image_size		,					)







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=FlaxRegNetModel(config=UpperCAmelCase__   )
										lowercase :							int    		=model(UpperCAmelCase__   )

										# Output shape (b, c, h, w)
										self.parent.assertEqual(
										    result.last_hidden_state.shape		,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,					)







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.num_labels
										lowercase :							Optional[Any]    		=FlaxRegNetForImageClassification(config=UpperCAmelCase__   )
										lowercase :							List[str]    		=model(UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase :							Any    		=config_and_inputs
										lowercase :							Tuple    		={'''pixel_values''': pixel_values}
										return config, inputs_dict





@require_flax
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							int    		=FlaxRegNetModelTester(self   )
										lowercase :							Tuple    		=ConfigTester(self		,					config_class=UpperCAmelCase__		,					has_text_modality=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__   )







				@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										pass







				@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Optional[Any]    		=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase :							Any    		=model_class(UpperCAmelCase__   )
																lowercase :							str    		=inspect.signature(model.__call__   )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowercase :							List[Any]    		=[*signature.parameters.keys()]

																lowercase :							List[str]    		=['''pixel_values''']
																self.assertListEqual(arg_names[:1]		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										def check_hidden_states_output(UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		Optional[int]   ):
																lowercase :							Union[str, Any]    		=model_class(UpperCAmelCase__   )

																lowercase :							Tuple    		=model(**self._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__   )   )

																lowercase :							Union[str, Any]    		=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																lowercase :							int    		=self.model_tester.num_stages
																self.assertEqual(len(UpperCAmelCase__   )		,					expected_num_stages + 1   )

										lowercase		,	lowercase :							Optional[int]    		=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase :							List[str]    		=True
																check_hidden_states_output(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																lowercase :							str    		=True

																check_hidden_states_output(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							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__   ):
																						lowercase :							List[Any]    		=self._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__   )
																						lowercase :							Optional[Any]    		=model_class(UpperCAmelCase__   )

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

																						with self.subTest('''JIT Enabled'''   ):
																												lowercase :							Union[str, Any]    		=model_jitted(**UpperCAmelCase__   ).to_tuple()

																						with self.subTest('''JIT Disabled'''   ):
																												with jax.disable_jit():
																																		lowercase :							int    		=model_jitted(**UpperCAmelCase__   ).to_tuple()

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






def 				_lowerCAmelCase			(   )				->  int:
						lowercase :							Dict    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
						return image







@require_flax
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@cached_property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''   ) if is_vision_available() else None







				@slow
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							int    		=FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''   )

										lowercase :							Tuple    		=self.default_image_processor
										lowercase :							Union[str, Any]    		=prepare_img()
										lowercase :							Any    		=image_processor(images=UpperCAmelCase__		,					return_tensors='''np'''   )

										lowercase :							Union[str, Any]    		=model(**UpperCAmelCase__   )

										# verify the logits
										lowercase :							str    		=(1, 1000)
										self.assertEqual(outputs.logits.shape		,					UpperCAmelCase__   )

										lowercase :							Dict    		=jnp.array([-0.41_80, -1.50_51, -3.48_36]   )

										self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,					UpperCAmelCase__		,					atol=1E-4   )   )








'''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


UpperCamelCase_    						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_    						=       """cuda""" if torch.cuda.is_available() else """cpu"""






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






def 				_lowerCAmelCase			(   __magic_name__						:     dict						)				->  dict:
						lowercase		,	lowercase :							int    		=[], []
						for title, text in zip(documents['''title''']   , documents['''text''']						):
												if text is not None:
																		for passage in split_text(__magic_name__						):
																								titles.append(title if title is not None else ''''''						)
																								texts.append(__magic_name__						)
						return {"title": titles, "text": texts}






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






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						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
						lowercase :							Tuple    		=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
						lowercase :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

						# And compute the embeddings
						lowercase :							Any    		=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__magic_name__						)
						lowercase :							Any    		=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						lowercase :							Optional[int]    		=Features(
						    {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
						lowercase :							Optional[Any]    		=dataset.map(
						    partial(__magic_name__   , ctx_encoder=__magic_name__   , ctx_tokenizer=__magic_name__						)   , batched=__magic_name__   , batch_size=processing_args.batch_size   , features=__magic_name__   , )

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# 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
						lowercase :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

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





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    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_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,							)
				lowerCamelCase_		       =    field(
				    default='facebook/rag-sequence-nq'						,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,							)
				lowerCamelCase_		       =    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_		       =    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 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={
				        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=16						,							metadata={
				        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
				    }						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    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)

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





