




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


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[Any]=100 ,     UpperCamelCase__				:      List[str]=" "      )     ->       List[str]:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      dict      )     ->       dict:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      dict ,     UpperCamelCase__				:      DPRContextEncoder ,     UpperCamelCase__				:      DPRContextEncoderTokenizerFast      )     ->       dict:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      "RagExampleArguments" ,     UpperCamelCase__				:      "ProcessingArguments" ,     UpperCamelCase__				:      "IndexHnswArguments" ,     )     ->       Union[str, Any]:





				"""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(UpperCamelCase__ ,     batched=UpperCamelCase__ ,     num_proc=processing_args.num_proc      )

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

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

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







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       field(
			    default=str(Path(__magic_name__ ).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv''' )			,				metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}			,				)
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}			,				)
			snake_case_   		=       field(
			    default='''facebook/rag-sequence-nq'''			,				metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}			,				)
			snake_case_   		=       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\''''
			        )
			    }			,				)
			snake_case_   		=       field(
			    default=str(Path(__magic_name__ ).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"""







			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={
			        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
			    }			,				)
			snake_case_   		=       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"""







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

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








import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
					    TimesformerForVideoClassification,
					    TimesformerModel,
					)
					from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from transformers import VideoMAEImageProcessor







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=10			,						lowerCamelCase__=3			,						lowerCamelCase__=2			,						lowerCamelCase__=2			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=32			,						lowerCamelCase__=5			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=10			,						lowerCamelCase__=0.02			,						lowerCamelCase__="divided_space_time"			,						lowerCamelCase__=None			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    patch_size
							__lowerCamelCase		       =    num_frames
							__lowerCamelCase		       =    is_training
							__lowerCamelCase		       =    use_labels
							__lowerCamelCase		       =    hidden_size
							__lowerCamelCase		       =    num_hidden_layers
							__lowerCamelCase		       =    num_attention_heads
							__lowerCamelCase		       =    intermediate_size
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    attention_type
							__lowerCamelCase		       =    initializer_range
							__lowerCamelCase		       =    scope
							__lowerCamelCase		       =    num_labels

							# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
							__lowerCamelCase		       =    (image_size // patch_size) ** 2
							__lowerCamelCase		       =    (num_frames) * self.num_patches_per_frame + 1


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    floats_tensor(
							    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

							__lowerCamelCase		       =    None
							if self.use_labels:
											__lowerCamelCase		       =    ids_tensor([self.batch_size]			,						self.num_labels							)

							__lowerCamelCase		       =    self.get_config()

							return config, pixel_values, labels


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



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerConfig(
							    image_size=self.image_size			,						patch_size=self.patch_size			,						num_channels=self.num_channels			,						num_frames=self.num_frames			,						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			,						initializer_range=self.initializer_range			,						attention_type=self.attention_type			,						)
							__lowerCamelCase		       =    self.num_labels
							return config


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__							)
							self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerForVideoClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()

							__lowerCamelCase		       =    model(lowerCamelCase__							)

							# verify the logits shape
							__lowerCamelCase		       =    torch.Size((self.batch_size, self.num_labels)							)
							self.parent.assertEqual(result.logits.shape			,						lowerCamelCase__							)






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    config_and_inputs
							__lowerCamelCase		       =    {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			snake_case_   		=       (
			    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(
							    self			,						config_class=lowerCamelCase__			,						has_text_modality=lowerCamelCase__			,						hidden_size=37							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=False							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    copy.deepcopy(lowerCamelCase__							)

							if return_labels:
											if model_class in get_values(lowerCamelCase__							):
															__lowerCamelCase		       =    torch.zeros(
															    self.model_tester.batch_size			,						dtype=torch.long			,						device=lowerCamelCase__							)

							return inputs_dict


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			@unittest.skip(reason='TimeSformer does not use inputs_embeds'							)
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)							)
											__lowerCamelCase		       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase__			,						nn.Linear							)							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											__lowerCamelCase		       =    inspect.signature(model.forward							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowerCamelCase		       =    [*signature.parameters.keys()]

											__lowerCamelCase		       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]			,						lowerCamelCase__							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_video_classification(*lowerCamelCase__							)


			@slow
			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    TimesformerModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							if not self.has_attentions:
											pass

							else:
											__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase		       =    True

											for model_class in self.all_model_classes:
															__lowerCamelCase		       =    self.model_tester.seq_length
															__lowerCamelCase		       =    self.model_tester.num_frames

															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    False
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    outputs.attentions
															self.assertEqual(len(lowerCamelCase__							)			,						self.model_tester.num_hidden_layers							)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    outputs.attentions
															self.assertEqual(len(lowerCamelCase__							)			,						self.model_tester.num_hidden_layers							)

															# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
															self.assertListEqual(
															    list(attentions[0].shape[-3:]							)			,						[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,						)
															__lowerCamelCase		       =    len(lowerCamelCase__							)

															# Check attention is always last and order is fine
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)

															self.assertEqual(out_len + 1			,						len(lowerCamelCase__							)							)

															__lowerCamelCase		       =    outputs.attentions

															self.assertEqual(len(lowerCamelCase__							)			,						self.model_tester.num_hidden_layers							)

															# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]							)			,						[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,						)






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							def check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											model.to(lowerCamelCase__							)
											model.eval()

											with torch.no_grad():
															__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)

											__lowerCamelCase		       =    outputs.hidden_states
											__lowerCamelCase		       =    self.model_tester.num_hidden_layers + 1
											self.assertEqual(len(lowerCamelCase__							)			,						lowerCamelCase__							)

											__lowerCamelCase		       =    self.model_tester.seq_length

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]							)			,						[seq_length, self.model_tester.hidden_size]			,						)

							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    True
											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__lowerCamelCase		       =    True

											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)







def 			lowerCamelCase_		(       )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video' ,     filename='eating_spaghetti.npy' ,     repo_type='dataset'      )
				__lowerCamelCase		       =    np.load(UpperCamelCase__      )
				return list(UpperCamelCase__      )







@require_torch
@require_vision
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			@cached_property
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							# logits were tested with a different mean and std, so we use the same here
							return (
							    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,						image_std=[0.5, 0.5, 0.5]							)
							    if is_vision_available()
							    else None
							)






			@slow
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'							).to(
							    lowerCamelCase__							)

							__lowerCamelCase		       =    self.default_image_processor
							__lowerCamelCase		       =    prepare_video()
							__lowerCamelCase		       =    image_processor(video[:8]			,						return_tensors='pt'							).to(lowerCamelCase__							)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase		       =    model(**lowerCamelCase__							)

							# verify the logits
							__lowerCamelCase		       =    torch.Size((1, 400)							)
							self.assertEqual(outputs.logits.shape			,						lowerCamelCase__							)

							__lowerCamelCase		       =    torch.tensor([-0.30_16, -0.77_13, -0.42_05]							).to(lowerCamelCase__							)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCamelCase__			,						atol=1e-4							)							)


