




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:
   lowercase__   :    Union[str, Any] 					=      None

if is_ray_available():
   import ray  # noqa: E402 # isort:skip
   from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
   lowercase__   :    Optional[Any] 					=      None
   lowercase__   :    Optional[Any] 					=      None
   lowercase__   :    Tuple 					=      None

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  Optional[int]:
     if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
          a											=   unittest.skip("test requires Datasets, Faiss, psutil")(__UpperCamelCase)
     return test_case





@require_distributed_retrieval
class     a__ (      UpperCamelCase__   ):







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



          '''simple docstring'''



          a											=   tempfile.mkdtemp()
          a											=   8

          # DPR tok
          a											=   [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a											=   os.path.join(self.tmpdirname						,	"dpr_tokenizer"				)
          os.makedirs(A						,	exist_ok=A				)
          a											=   os.path.join(A						,	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
          a											=   [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          a											=   dict(zip(A						,	range(len(A				)				)				)				)
          a											=   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          a											=   {"unk_token": "<unk>"}

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







     def     lowerCAmelCase_       (			self				)						->					DPRQuestionEncoderTokenizer:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					BartTokenizer:



          '''simple docstring'''



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







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



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname				)







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          a											=   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     lowerCAmelCase_       (			self						,	A						,	A=12345				)						->					RagPyTorchDistributedRetriever:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A				)						->					RagRayDistributedRetriever:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A=12345				)						->					Any:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A				)						->					List[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					None:



          '''simple docstring'''



          a						,   a						,   a											=   retriever.retrieve(A						,	n_docs=A				)
          self.assertEqual(retrieved_doc_embeds.shape						,	(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(A				)						,	2				)
          self.assertEqual(sorted(doc_dicts[0]				)						,	["embeddings", "id", "text", "title"]				)
          self.assertEqual(len(doc_dicts[0]["id"]				)						,	A				)
          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     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   1
          a											=   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=A				)						,	A						,	A				)







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          a											=   1
          a											=   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=A						,	from_disk=A				)						,	A						,	A						,	)







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



          '''simple docstring'''



          a											=   1
          a											=   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=A						,	from_disk=A				)						,	A						,	A						,	)







     @require_ray
     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          a											=   1
          a											=   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=A				)						,	A						,	A				)
          ray.shutdown()







     @require_ray
     def     lowerCAmelCase_       (			self				)						->					List[Any]:



          '''simple docstring'''



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







     @require_ray
     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   1
          a											=   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=A						,	from_disk=A				)						,	A						,	A				)
          ray.shutdown()







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







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



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

          # Test a single int value which represents the shortest edge
          a											=   get_size_dict(224						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          # Test a tuple of ints which represents (height, width)
          a											=   get_size_dict((150, 200)				)
          self.assertEqual(A						,	{"height": 150, "width": 200}				)

          # Test a tuple of ints which represents (width, height)
          a											=   get_size_dict((150, 200)						,	height_width_order=A				)
          self.assertEqual(A						,	{"height": 200, "width": 150}				)

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

          # Test int with default_to_square=True and max_size fails
          with self.assertRaises(A				):
               get_size_dict(224						,	max_size=256						,	default_to_square=A				)

