




'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	lowercase_					(				_UpperCAmelCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		(KDPMaAncestralDiscreteScheduler,)
   lowerCamelCase_       						=		10



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   {
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**__lowerCamelCase					)
      return config



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""






      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""






      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,   [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
         self.check_over_configs(beta_start=__lowerCamelCase						,   beta_end=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""






      for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""






      if torch_device == "mps":
         return
      _SCREAMING_SNAKE_CASE =   self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE =   self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE =   scheduler_class(**__lowerCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps					)

      _SCREAMING_SNAKE_CASE =   torch.manual_seed(0					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter * scheduler.init_noise_sigma
      _SCREAMING_SNAKE_CASE =   sample.to(__lowerCamelCase					)

      for i, t in enumerate(scheduler.timesteps					):
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   generator=__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7					) < 1e-2
      assert abs(result_mean.item() - 1_8.0_3_3_1					) < 5e-3



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""






      for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""






      if torch_device == "mps":
         return
      _SCREAMING_SNAKE_CASE =   self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE =   self.get_scheduler_config(prediction_type="v_prediction"					)
      _SCREAMING_SNAKE_CASE =   scheduler_class(**__lowerCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter * scheduler.init_noise_sigma
      _SCREAMING_SNAKE_CASE =   sample.to(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.manual_seed(0					)

      for i, t in enumerate(scheduler.timesteps					):
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   generator=__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 3_2_8.9_9_7_0					) < 1e-2
      assert abs(result_mean.item() - 0.4_2_8_4					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""






      if torch_device == "mps":
         return
      _SCREAMING_SNAKE_CASE =   self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE =   self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE =   scheduler_class(**__lowerCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps						,   device=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   torch.manual_seed(0					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter.to(__lowerCamelCase					) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   generator=__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8					) < 1e-1
      assert abs(result_mean.item() - 1_8.0_3_3_1					) < 1e-3







'''simple docstring'''



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:
 lowerCamelCase_	 =		None

if is_ray_available():
 import ray  # noqa: E402 # isort:skip
 from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
 lowerCamelCase_	 =		None
 lowerCamelCase_	 =		None
 lowerCamelCase_	 =		None
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Any						)				->				Union[str, Any]:
   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      _SCREAMING_SNAKE_CASE =   unittest.skip("test requires Datasets, Faiss, psutil"						)(__A						)
   return test_case







@require_distributed_retrieval
class 	lowercase_					(				A     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   tempfile.mkdtemp()
      _SCREAMING_SNAKE_CASE =   8

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

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



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

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



   def 							lowerCAmelCase_    (					self      :			Any					):

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



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      shutil.rmtree(self.tmpdirname					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			Optional[Any]=1_2_3_4_5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   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:
         _SCREAMING_SNAKE_CASE =   dataset
         _SCREAMING_SNAKE_CASE =   RagPyTorchDistributedRetriever(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   )
         if init_retrieval:
            retriever.init_retrieval(__lowerCamelCase					)
      return retriever



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			bool					):

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



   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			List[str]=1_2_3_4_5					):

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



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool					):

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



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			RagRetriever						,   __lowerCamelCase      :			np.array						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   retriever.retrieve(__lowerCamelCase						,   n_docs=__lowerCamelCase					)
      self.assertEqual(retrieved_doc_embeds.shape						,   (2, n_docs, self.retrieval_vector_size)					)
      self.assertEqual(len(__lowerCamelCase					)						,   2					)
      self.assertEqual(sorted(doc_dicts[0]					)						,   ["embeddings", "id", "text", "title"]					)
      self.assertEqual(len(doc_dicts[0]["id"]					)						,   __lowerCamelCase					)
      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      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   )



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   )



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase					)
      ray.shutdown()



   @require_ray
   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   np.array(
          [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]						,   dtype=np.floataa					)
      with self.assertRaises(__lowerCamelCase					):
         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   )
      ray.shutdown()



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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=__lowerCamelCase						,   from_disk=__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase					)
      ray.shutdown()

