





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
				from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
				snake_case_				=      None

if is_ray_available():
				import ray  # noqa: E402 # isort:skip
				from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
				snake_case_				=      None
				snake_case_				=      None
				snake_case_				=      None


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Dict:
		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
				lowerCamelCase :			Dict    		=unittest.skip('''test requires Datasets, Faiss, psutil'''		)(SCREAMING_SNAKE_CASE_		)
		return test_case



@require_distributed_retrieval
class 							snake_case_		(      _A):







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			List[str]    		=tempfile.mkdtemp()
				lowerCamelCase :			Dict    		=8

				# DPR tok
				lowerCamelCase :			Union[str, Any]    		=[
				    '''[UNK]''',
				    '''[CLS]''',
				    '''[SEP]''',
				    '''[PAD]''',
				    '''[MASK]''',
				    '''want''',
				    '''##want''',
				    '''##ed''',
				    '''wa''',
				    '''un''',
				    '''runn''',
				    '''##ing''',
				    ''',''',
				    '''low''',
				    '''lowest''',
				]
				lowerCamelCase :			int    		=os.path.join(self.tmpdirname      ,  '''dpr_tokenizer''' )
				os.makedirs(__lowercase      ,  exist_ok=__lowercase )
				lowerCamelCase :			List[str]    		=os.path.join(__lowercase      ,  DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
				with open(self.vocab_file      ,  '''w'''      ,  encoding='''utf-8''' ) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

				# BART tok
				lowerCamelCase :			List[str]    		=[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''\u0120''',
				    '''\u0120l''',
				    '''\u0120n''',
				    '''\u0120lo''',
				    '''\u0120low''',
				    '''er''',
				    '''\u0120lowest''',
				    '''\u0120newer''',
				    '''\u0120wider''',
				    '''<unk>''',
				]
				lowerCamelCase :			int    		=dict(zip(__lowercase      ,  range(len(__lowercase ) ) ) )
				lowerCamelCase :			Tuple    		=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
				lowerCamelCase :			List[Any]    		={'''unk_token''': '''<unk>'''}

				lowerCamelCase :			Optional[int]    		=os.path.join(self.tmpdirname      ,  '''bart_tokenizer''' )
				os.makedirs(__lowercase      ,  exist_ok=__lowercase )
				lowerCamelCase :			Tuple    		=os.path.join(__lowercase      ,  BART_VOCAB_FILES_NAMES['''vocab_file'''] )
				lowerCamelCase :			Optional[Any]    		=os.path.join(__lowercase      ,  BART_VOCAB_FILES_NAMES['''merges_file'''] )
				with open(self.vocab_file      ,  '''w'''      ,  encoding='''utf-8''' ) as fp:
						fp.write(json.dumps(__lowercase ) + '''\n''' )
				with open(self.merges_file      ,  '''w'''      ,  encoding='''utf-8''' ) as fp:
						fp.write('''\n'''.join(__lowercase ) )







		def       __lowercase (		self )					-> DPRQuestionEncoderTokenizer:
				return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,  '''dpr_tokenizer''' ) )







		def       __lowercase (		self )					-> BartTokenizer:
				return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,  '''bart_tokenizer''' ) )







		def       __lowercase (		self )					-> Tuple:
				shutil.rmtree(self.tmpdirname )







		def       __lowercase (		self )					-> List[Any]:
				lowerCamelCase :			Dict    		=Dataset.from_dict(
				    {
				        '''id''': ['''0''', '''1'''],
				        '''text''': ['''foo''', '''bar'''],
				        '''title''': ['''Foo''', '''Bar'''],
				        '''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
				    } )
				dataset.add_faiss_index('''embeddings'''      ,  string_factory='''Flat'''      ,  metric_type=faiss.METRIC_INNER_PRODUCT )
				return dataset







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase=1_2_3_4_5 )					-> RagPyTorchDistributedRetriever:
				lowerCamelCase :			Tuple    		=self.get_dummy_dataset()
				lowerCamelCase :			int    		=RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  )
				with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
						lowerCamelCase :			Optional[Any]    		=dataset
						lowerCamelCase :			Tuple    		=RagPyTorchDistributedRetriever(
						    __lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  )
						if init_retrieval:
								retriever.init_retrieval(__lowercase )
				return retriever







		def       __lowercase (		self      ,  __lowercase )					-> RagRayDistributedRetriever:
				# Have to run in local mode because sys.path modifications at top of
				# file are not propogated to remote workers.
				# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
				ray.init(local_mode=__lowercase )
				lowerCamelCase :			int    		=RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  )
				lowerCamelCase :			Dict    		=ray.remote(__lowercase )
				lowerCamelCase :			Dict    		=[remote_cls.remote() for _ in range(1 )]
				with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
						lowerCamelCase :			Any    		=self.get_dummy_dataset()
						lowerCamelCase :			Optional[Any]    		=RagRayDistributedRetriever(
						    __lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  retrieval_workers=__lowercase      ,  )
						if init_retrieval:
								retriever.init_retrieval()
				return retriever







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase=1_2_3_4_5 )					-> Optional[int]:
				lowerCamelCase :			Any    		=self.get_dummy_dataset()
				lowerCamelCase :			Optional[Any]    		=RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  index_name='''custom'''      ,  )
				if from_disk:
						lowerCamelCase :			List[str]    		=os.path.join(self.tmpdirname      ,  '''dataset''' )
						lowerCamelCase :			Optional[Any]    		=os.path.join(self.tmpdirname      ,  '''index.faiss''' )
						dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname      ,  '''index.faiss''' ) )
						dataset.drop_index('''embeddings''' )
						dataset.save_to_disk(os.path.join(self.tmpdirname      ,  '''dataset''' ) )
						del dataset
						lowerCamelCase :			Tuple    		=RagPyTorchDistributedRetriever(
						    __lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  )
				else:
						lowerCamelCase :			Optional[Any]    		=RagPyTorchDistributedRetriever(
						    __lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  index=CustomHFIndex(config.retrieval_vector_size      ,  __lowercase )      ,  )
				if init_retrieval:
						retriever.init_retrieval(__lowercase )
				return retriever







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase )					-> Optional[int]:
				# Have to run in local mode because sys.path modifications at top of
				# file are not propogated to remote workers.
				# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
				ray.init(local_mode=__lowercase )
				lowerCamelCase :			Any    		=self.get_dummy_dataset()
				lowerCamelCase :			Any    		=RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  index_name='''custom'''      ,  )
				lowerCamelCase :			List[Any]    		=ray.remote(__lowercase )
				lowerCamelCase :			Any    		=[remote_cls.remote() for _ in range(1 )]
				if from_disk:
						lowerCamelCase :			str    		=os.path.join(self.tmpdirname      ,  '''dataset''' )
						lowerCamelCase :			Optional[int]    		=os.path.join(self.tmpdirname      ,  '''index.faiss''' )
						dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname      ,  '''index.faiss''' ) )
						dataset.drop_index('''embeddings''' )
						dataset.save_to_disk(os.path.join(self.tmpdirname      ,  '''dataset''' ) )
						del dataset
						lowerCamelCase :			Optional[Any]    		=RagRayDistributedRetriever(
						    __lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  retrieval_workers=__lowercase      ,  index=CustomHFIndex.load_from_disk(
						        vector_size=config.retrieval_vector_size      ,  dataset_path=config.passages_path      ,  index_path=config.index_path      ,  )      ,  )
				else:
						lowerCamelCase :			Optional[int]    		=RagRayDistributedRetriever(
						    __lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  retrieval_workers=__lowercase      ,  index=CustomHFIndex(config.retrieval_vector_size      ,  __lowercase )      ,  )
				if init_retrieval:
						retriever.init_retrieval()
				return retriever







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> None:
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Any    		=retriever.retrieve(__lowercase      ,  n_docs=__lowercase )
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size) )
				self.assertEqual(len(__lowercase )      ,  2 )
				self.assertEqual(sorted(doc_dicts[0] )      ,  ['''embeddings''', '''id''', '''text''', '''title'''] )
				self.assertEqual(len(doc_dicts[0]['''id'''] )      ,  __lowercase )
				self.assertEqual(doc_dicts[0]['''id'''][0]      ,  '''1''' )  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['''id'''][0]      ,  '''0''' )  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()      ,  [[1], [0]] )







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			Union[str, Any]    		=1
				lowerCamelCase :			Any    		=np.array(
				    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]      ,  dtype=np.floataa )

				self.distributed_retriever_check(
				    self.get_dummy_pytorch_distributed_retriever(init_retrieval=__lowercase )      ,  __lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			Any    		=1
				lowerCamelCase :			Tuple    		=np.array(
				    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]      ,  dtype=np.floataa )

				self.distributed_retriever_check(
				    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=__lowercase      ,  from_disk=__lowercase )      ,  __lowercase      ,  __lowercase      ,  )







		def       __lowercase (		self )					-> int:
				lowerCamelCase :			Union[str, Any]    		=1
				lowerCamelCase :			Optional[int]    		=np.array(
				    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]      ,  dtype=np.floataa )

				self.distributed_retriever_check(
				    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=__lowercase      ,  from_disk=__lowercase )      ,  __lowercase      ,  __lowercase      ,  )







		@require_ray
		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			Optional[int]    		=1
				lowerCamelCase :			Dict    		=np.array(
				    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]      ,  dtype=np.floataa )

				self.distributed_retriever_check(
				    self.get_dummy_ray_distributed_retriever(init_retrieval=__lowercase )      ,  __lowercase      ,  __lowercase )
				ray.shutdown()







		@require_ray
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Optional[int]    		=1
				lowerCamelCase :			List[Any]    		=np.array(
				    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]      ,  dtype=np.floataa )
				with self.assertRaises(__lowercase ):
						self.distributed_retriever_check(
						    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__lowercase      ,  from_disk=__lowercase )      ,  __lowercase      ,  __lowercase      ,  )
				ray.shutdown()







		@require_ray
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			List[Any]    		=1
				lowerCamelCase :			Union[str, Any]    		=np.array(
				    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]      ,  dtype=np.floataa )

				self.distributed_retriever_check(
				    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__lowercase      ,  from_disk=__lowercase )      ,  __lowercase      ,  __lowercase )
				ray.shutdown()








import sys

from transformers import AutoTokenizer


snake_case_				=      sys.argv[1]
snake_case_				=      sys.argv[2]
snake_case_				=      int(sys.argv[3])

snake_case_				=      0

snake_case_				=      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
				for line in f_p:
								snake_case_				=      line.rstrip()

								if not line:
												print(line)
												snake_case_				=      0
												continue

								snake_case_				=      line.split()[0]

								snake_case_				=      len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('''''')
												print(line)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

