






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:
		_a				     =  None

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


def __A   (				__lowerCAmelCase     )->  Optional[int]:





						"""simple docstring"""



						if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
												_UpperCAmelCase     =      unittest.skip('test requires Datasets, Faiss, psutil'     )(__lowerCAmelCase     )
						return test_case





@require_distributed_retrieval
class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      tempfile.mkdtemp()
							_UpperCAmelCase     =      8

							# DPR tok
							_UpperCAmelCase     =      [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							_UpperCAmelCase     =      os.path.join(self.tmpdirname	,   'dpr_tokenizer'   )
							os.makedirs(UpperCAmelCase	,   exist_ok=UpperCAmelCase   )
							_UpperCAmelCase     =      os.path.join(UpperCAmelCase	,   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
							_UpperCAmelCase     =      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							_UpperCAmelCase     =      dict(zip(UpperCAmelCase	,   range(len(UpperCAmelCase   )   )   )   )
							_UpperCAmelCase     =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							_UpperCAmelCase     =      {'unk_token': '<unk>'}

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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,   'dpr_tokenizer'   )   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,   'bart_tokenizer'   )   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      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 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase=1_2345   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_dummy_dataset()
							_UpperCAmelCase     =      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:
													_UpperCAmelCase     =      dataset
													_UpperCAmelCase     =      RagPyTorchDistributedRetriever(
													    UpperCAmelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   )
													if init_retrieval:
																			retriever.init_retrieval(UpperCAmelCase   )
							return retriever

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							ray.init(local_mode=UpperCAmelCase   )
							_UpperCAmelCase     =      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size	,   question_encoder=DPRConfig().to_dict()	,   generator=BartConfig().to_dict()	,   )
							_UpperCAmelCase     =      ray.remote(UpperCAmelCase   )
							_UpperCAmelCase     =      [remote_cls.remote() for _ in range(1   )]
							with patch('transformers.models.rag.retrieval_rag.load_dataset'   ) as mock_load_dataset:
													_UpperCAmelCase     =      self.get_dummy_dataset()
													_UpperCAmelCase     =      RagRayDistributedRetriever(
													    UpperCAmelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   retrieval_workers=UpperCAmelCase	,   )
													if init_retrieval:
																			retriever.init_retrieval()
							return retriever

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase=1_2345   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_dummy_dataset()
							_UpperCAmelCase     =      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size	,   question_encoder=DPRConfig().to_dict()	,   generator=BartConfig().to_dict()	,   index_name='custom'	,   )
							if from_disk:
													_UpperCAmelCase     =      os.path.join(self.tmpdirname	,   'dataset'   )
													_UpperCAmelCase     =      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
													_UpperCAmelCase     =      RagPyTorchDistributedRetriever(
													    UpperCAmelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   )
							else:
													_UpperCAmelCase     =      RagPyTorchDistributedRetriever(
													    UpperCAmelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   index=CustomHFIndex(config.retrieval_vector_size	,   UpperCAmelCase   )	,   )
							if init_retrieval:
													retriever.init_retrieval(UpperCAmelCase   )
							return retriever

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							ray.init(local_mode=UpperCAmelCase   )
							_UpperCAmelCase     =      self.get_dummy_dataset()
							_UpperCAmelCase     =      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size	,   question_encoder=DPRConfig().to_dict()	,   generator=BartConfig().to_dict()	,   index_name='custom'	,   )
							_UpperCAmelCase     =      ray.remote(UpperCAmelCase   )
							_UpperCAmelCase     =      [remote_cls.remote() for _ in range(1   )]
							if from_disk:
													_UpperCAmelCase     =      os.path.join(self.tmpdirname	,   'dataset'   )
													_UpperCAmelCase     =      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
													_UpperCAmelCase     =      RagRayDistributedRetriever(
													    UpperCAmelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   retrieval_workers=UpperCAmelCase	,   index=CustomHFIndex.load_from_disk(
													        vector_size=config.retrieval_vector_size	,   dataset_path=config.passages_path	,   index_path=config.index_path	,   )	,   )
							else:
													_UpperCAmelCase     =      RagRayDistributedRetriever(
													    UpperCAmelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   retrieval_workers=UpperCAmelCase	,   index=CustomHFIndex(config.retrieval_vector_size	,   UpperCAmelCase   )	,   )
							if init_retrieval:
													retriever.init_retrieval()
							return retriever

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      retriever.retrieve(UpperCAmelCase	,   n_docs=UpperCAmelCase   )
							self.assertEqual(retrieved_doc_embeds.shape	,   (2, n_docs, self.retrieval_vector_size)   )
							self.assertEqual(len(UpperCAmelCase   )	,   2   )
							self.assertEqual(sorted(doc_dicts[0]   )	,   ['embeddings', 'id', 'text', 'title']   )
							self.assertEqual(len(doc_dicts[0]['id']   )	,   UpperCAmelCase   )
							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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      1
							_UpperCAmelCase     =      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=UpperCAmelCase   )	,   UpperCAmelCase	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      1
							_UpperCAmelCase     =      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=UpperCAmelCase	,   from_disk=UpperCAmelCase   )	,   UpperCAmelCase	,   UpperCAmelCase	,   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      1
							_UpperCAmelCase     =      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=UpperCAmelCase	,   from_disk=UpperCAmelCase   )	,   UpperCAmelCase	,   UpperCAmelCase	,   )

	@require_ray
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      1
							_UpperCAmelCase     =      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=UpperCAmelCase   )	,   UpperCAmelCase	,   UpperCAmelCase   )
							ray.shutdown()

	@require_ray
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      1
							_UpperCAmelCase     =      np.array(
							    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]	,   dtype=np.floataa   )
							with self.assertRaises(UpperCAmelCase   ):
													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=UpperCAmelCase	,   from_disk=UpperCAmelCase   )	,   UpperCAmelCase	,   UpperCAmelCase	,   )
							ray.shutdown()

	@require_ray
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      1
							_UpperCAmelCase     =      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=UpperCAmelCase	,   from_disk=UpperCAmelCase   )	,   UpperCAmelCase	,   UpperCAmelCase   )
							ray.shutdown()








import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	UpperCamelCase__								=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							if isinstance(model.config	,   UpperCAmelCase   ):
													_UpperCAmelCase ,			_UpperCAmelCase     =      list(tokenizer.lang_code_to_id.keys()   )[:2]
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang=UpperCAmelCase	,   tgt_lang=UpperCAmelCase   )
							else:
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							return translator, ["Some string", "Some other text"]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      translator('Some string'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string', 'other string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}, {'translation_text': ANY(UpperCAmelCase   )}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation_cn_to_ar'   )

							# but we do for this one
							_UpperCAmelCase     =      pipeline(task='translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )
							_UpperCAmelCase     =      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )

							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							# Missing src_lang, tgt_lang
							with self.assertRaises(UpperCAmelCase   ):
													translator('This is a test'   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='hi_IN'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'यह एक परीक्षण है'}]   )

							# src_lang, tgt_lang can be defined at pipeline call time
							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							_UpperCAmelCase     =      translator('This is a test'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							_UpperCAmelCase     =      pipeline(task='translation_cn_to_ar'	,   model=UpperCAmelCase   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'cn'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'ar'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							with pytest.warns(UpperCAmelCase	,   match=R'.*translation_en_to_de.*'   ):
													_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase   )
							self.assertEqual(translator.task	,   'translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation'   )
