

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  :Optional[Any]										=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
__a  :List[str]										=       'cuda' if torch.cuda.is_available() else 'cpu'

def 	__snake_case		(      __UpperCamelCase     :      str							,__UpperCamelCase     :      List[Any]=100							,__UpperCamelCase     :      Optional[int]=" "  ):



      """simple docstring"""

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

def 	__snake_case		(      __UpperCamelCase     :      dict  ):



      """simple docstring"""

      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 	__snake_case		(      __UpperCamelCase     :      dict							,__UpperCamelCase     :      DPRContextEncoder							,__UpperCamelCase     :      DPRContextEncoderTokenizerFast  ):



      """simple docstring"""

      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 	__snake_case		(      __UpperCamelCase     :      "RagExampleArguments"							,__UpperCamelCase     :      "ProcessingArguments"							,__UpperCamelCase     :      "IndexHnswArguments"							,):



      """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
      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

@dataclass
class    _a :




      """simple docstring"""

      _lowerCamelCase    :       str							=    field(
          default=str(Path(__SCREAMING_SNAKE_CASE							).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=__SCREAMING_SNAKE_CASE			,							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(__SCREAMING_SNAKE_CASE							).parent / 'test_run' / 'dummy-kb'							)			,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}			,							)




@dataclass
class    _a :




      """simple docstring"""

      _lowerCamelCase    :       Optional[int]							=    field(
          default=__SCREAMING_SNAKE_CASE			,							metadata={
              'help': 'The number of processes to use to split the documents into passages. Default is single process.'
          }			,							)
      _lowerCamelCase    :       int							=    field(
          default=1_6			,							metadata={
              'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
          }			,							)




@dataclass
class    _a :




      """simple docstring"""

      _lowerCamelCase    :       int							=    field(
          default=7_6_8			,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}			,							)
      _lowerCamelCase    :       int							=    field(
          default=1_2_8			,							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  :Optional[Any]										=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __a					,	__a					,	__a  :str										=       parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            __a  :str										=       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 EsmConfig, 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 numpy
				import tensorflow as tf

				from transformers.models.esm.modeling_tf_esm import (
				    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEsmForMaskedLM,
				    TFEsmForSequenceClassification,
				    TFEsmForTokenClassification,
				    TFEsmModel,
				)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, ):
				__a           =			parent
				__a           =			13
				__a           =			7
				__a           =			True
				__a           =			True
				__a           =			True
				__a           =			99
				__a           =			32
				__a           =			2
				__a           =			4
				__a           =			37
				__a           =			'''gelu'''
				__a           =			0.1
				__a           =			0.1
				__a           =			512
				__a           =			16
				__a           =			2
				__a           =			0.02
				__a           =			3
				__a           =			4
				__a           =			None

		def 			__UpperCAmelCase       (  self      ):
				__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]      )

				__a           =			None
				__a           =			None
				__a           =			None
				if self.use_labels:
						__a           =			ids_tensor([self.batch_size]							, self.type_sequence_label_size      )
						__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.num_labels      )
						__a           =			ids_tensor([self.batch_size]							, self.num_choices      )

				__a           =			EsmConfig(
				    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, pad_token_id=1							, 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							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, initializer_range=self.initializer_range							, )

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

		def 			__UpperCAmelCase       (  self      ):
				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			self.prepare_config_and_inputs()

				__a           =			True
				__a           =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2      )

				return (
				    config,
				    input_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			TFEsmModel(config=_a      )
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				__a           =			model(_a      )

				__a           =			[input_ids, input_mask]
				__a           =			model(_a      )

				__a           =			model(_a      )

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, ):
				__a           =			True

				__a           =			TFEsmModel(config=_a      )
				__a           =			{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''encoder_hidden_states''': encoder_hidden_states,
				    '''encoder_attention_mask''': encoder_attention_mask,
				}
				__a           =			model(_a      )

				__a           =			[input_ids, input_mask]
				__a           =			model(_a							, encoder_hidden_states=_a      )

				# Also check the case where encoder outputs are not passed
				__a           =			model(_a							, attention_mask=_a      )

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			TFEsmForMaskedLM(config=_a      )
				__a           =			model([input_ids, input_mask]      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			self.num_labels
				__a           =			TFEsmForTokenClassification(config=_a      )
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				__a           =			model(_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)      )





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







@require_tf
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				(
		    (
		        TFEsmModel,
		        TFEsmForMaskedLM,
		        TFEsmForSequenceClassification,
		        TFEsmForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__UpperCAmelCase		:			Tuple										=				(
		    {
		        'feature-extraction': TFEsmModel,
		        'fill-mask': TFEsmForMaskedLM,
		        'text-classification': TFEsmForSequenceClassification,
		        'token-classification': TFEsmForTokenClassification,
		        'zero-shot': TFEsmForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__UpperCAmelCase		:			Tuple										=				False
		__UpperCAmelCase		:			Union[str, Any]										=				False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, hidden_size=37      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_a      )

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			TFEsmModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass





		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__a           =			model_class(_a      )
						assert isinstance(model.get_input_embeddings()							, tf.keras.layers.Layer      )
						if model_class is TFEsmForMaskedLM:
								# Output embedding test differs from the main test because they're a matrix, not a layer
								__a           =			model.get_bias()
								assert isinstance(_a							, _a      )
								for k, v in name.items():
										assert isinstance(_a							, tf.Variable      )
						else:
								__a           =			model.get_output_embeddings()
								assert x is None
								__a           =			model.get_bias()
								assert name is None







@require_tf
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )

				__a           =			tf.constant([[0, 1, 2, 3, 4, 5]]      )
				__a           =			model(_a      )[0]
				__a           =			[1, 6, 33]
				self.assertEqual(list(output.numpy().shape      )							, _a      )
				# compare the actual values for a slice.
				__a           =			tf.constant(
				    [
				        [
				            [8.92_1518, -10.58_9814, -6.467_1307],
				            [-6.396_7156, -13.91_1377, -1.121_1915],
				            [-7.78_1247, -13.95_1557, -3.74_0592],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-2      )      )





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )

				__a           =			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
				__a           =			model(_a      )[0]
				# compare the actual values for a slice.
				__a           =			tf.constant(
				    [
				        [
				            [0.1444_3092, 0.5412_5327, 0.324_7739],
				            [0.3034_0484, 0.0052_6676, 0.3107_7722],
				            [0.3227_8043, -0.2498_7096, 0.341_4628],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-4      )      )






