import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


A     :  Optional[int]					     =       pytest.mark.integration


@require_faiss
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       int   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  Dataset.from_dict({"filename": ["my_name-train" + "_" + str(__magic_name__   ) for x in np.arange(30   ).tolist()]}   )
        return dset







  def    __A     (							self :       Optional[int]   )  ->	Tuple:
        import faiss

        SCREAMING_SNAKE_CASE_          =  self._create_dummy_dataset()
        SCREAMING_SNAKE_CASE_          =  dset.map(
            lambda __magic_name__  ,     __magic_name__   : {"vecs": i * np.ones(5  ,     dtype=np.floataa   )}  ,     with_indices=__magic_name__  ,     keep_in_memory=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  dset.add_faiss_index("vecs"  ,     batch_size=100  ,     metric_type=faiss.METRIC_INNER_PRODUCT   )
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  dset.get_nearest_examples("vecs"  ,     np.ones(5  ,     dtype=np.floataa   )   )
        self.assertEqual(examples["filename"][0]  ,     "my_name-train_29"   )
        dset.drop_index("vecs"   )







  def    __A     (							self :       Dict   )  ->	List[Any]:
        import faiss

        SCREAMING_SNAKE_CASE_          =  self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1  ,     1   )  ,     index_name="vecs"  ,     batch_size=100  ,     metric_type=faiss.METRIC_INNER_PRODUCT  ,     )
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  dset.get_nearest_examples("vecs"  ,     np.ones(5  ,     dtype=np.floataa   )   )
        self.assertEqual(examples["filename"][0]  ,     "my_name-train_29"   )







  def    __A     (							self :       Tuple   )  ->	Any:
        import faiss

        SCREAMING_SNAKE_CASE_          =  self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1  ,     1   )  ,     index_name="vecs"  ,     metric_type=faiss.METRIC_INNER_PRODUCT  ,     )

        # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
        # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
        # see https://bugs.python.org/issue14243 and
        # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
        with tempfile.NamedTemporaryFile(delete=__magic_name__   ) as tmp_file:
              dset.save_faiss_index("vecs"  ,     tmp_file.name   )
              dset.load_faiss_index("vecs2"  ,     tmp_file.name   )
        os.unlink(tmp_file.name   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  dset.get_nearest_examples("vecs2"  ,     np.ones(5  ,     dtype=np.floataa   )   )
        self.assertEqual(examples["filename"][0]  ,     "my_name-train_29"   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)   ) * np.arange(30   ).reshape(-1  ,     1   )  ,     index_name="vecs"   )
        dset.drop_index("vecs"   )
        self.assertRaises(__magic_name__  ,     partial(dset.get_nearest_examples  ,     "vecs2"  ,     np.ones(5  ,     dtype=np.floataa   )   )   )







  def    __A     (							self :       int   )  ->	Union[str, Any]:
        from elasticsearch import Elasticsearch

        SCREAMING_SNAKE_CASE_          =  self._create_dummy_dataset()
        with patch("elasticsearch.Elasticsearch.search"   ) as mocked_search, patch(
            "elasticsearch.client.IndicesClient.create"   ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"   ) as mocked_bulk:
              SCREAMING_SNAKE_CASE_          =  {"acknowledged": True}
              mocked_bulk.return_value([(True, None)] * 30   )
              SCREAMING_SNAKE_CASE_          =  {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
              SCREAMING_SNAKE_CASE_          =  Elasticsearch()

              dset.add_elasticsearch_index("filename"  ,     es_client=__magic_name__   )
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  dset.get_nearest_examples("filename"  ,     "my_name-train_29"   )
              self.assertEqual(examples["filename"][0]  ,     "my_name-train_29"   )



@require_faiss
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       int   )  ->	List[Any]:
        import faiss

        SCREAMING_SNAKE_CASE_          =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )

        # add vectors
        index.add_vectors(np.eye(5  ,     dtype=np.floataa   )   )
        self.assertIsNotNone(index.faiss_index   )
        self.assertEqual(index.faiss_index.ntotal  ,     5   )
        index.add_vectors(np.zeros((5, 5)  ,     dtype=np.floataa   )   )
        self.assertEqual(index.faiss_index.ntotal  ,     10   )

        # single query
        SCREAMING_SNAKE_CASE_          =  np.zeros(5  ,     dtype=np.floataa   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search(__magic_name__   )
        self.assertRaises(__magic_name__  ,     index.search  ,     query.reshape(-1  ,     1   )   )
        self.assertGreater(scores[0]  ,     0   )
        self.assertEqual(indices[0]  ,     1   )

        # batched queries
        SCREAMING_SNAKE_CASE_          =  np.eye(5  ,     dtype=np.floataa   )[::-1]
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search_batch(__magic_name__   )
        self.assertRaises(__magic_name__  ,     index.search_batch  ,     queries[0]   )
        SCREAMING_SNAKE_CASE_          =  [scores[0] for scores in total_scores]
        SCREAMING_SNAKE_CASE_          =  [indices[0] for indices in total_indices]
        self.assertGreater(np.min(__magic_name__   )  ,     0   )
        self.assertListEqual([4, 3, 2, 1, 0]  ,     __magic_name__   )







  def    __A     (							self :       int   )  ->	List[str]:
        import faiss

        SCREAMING_SNAKE_CASE_          =  FaissIndex(string_factory="Flat"   )
        index.add_vectors(np.eye(5  ,     dtype=np.floataa   )   )
        self.assertIsInstance(index.faiss_index  ,     faiss.IndexFlat   )
        SCREAMING_SNAKE_CASE_          =  FaissIndex(string_factory="LSH"   )
        index.add_vectors(np.eye(5  ,     dtype=np.floataa   )   )
        self.assertIsInstance(index.faiss_index  ,     faiss.IndexLSH   )
        with self.assertRaises(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  FaissIndex(string_factory="Flat"  ,     custom_index=faiss.IndexFlat(5   )   )







  def    __A     (							self :       Dict   )  ->	Union[str, Any]:
        import faiss

        SCREAMING_SNAKE_CASE_          =  faiss.IndexFlat(5   )
        SCREAMING_SNAKE_CASE_          =  FaissIndex(custom_index=__magic_name__   )
        index.add_vectors(np.eye(5  ,     dtype=np.floataa   )   )
        self.assertIsInstance(index.faiss_index  ,     faiss.IndexFlat   )







  def    __A     (							self :       List[Any]   )  ->	Union[str, Any]:
        import faiss

        SCREAMING_SNAKE_CASE_          =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )
        index.add_vectors(np.eye(5  ,     dtype=np.floataa   )   )

        # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
        # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
        # see https://bugs.python.org/issue14243 and
        # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
        with tempfile.NamedTemporaryFile(delete=__magic_name__   ) as tmp_file:
              index.save(tmp_file.name   )
              SCREAMING_SNAKE_CASE_          =  FaissIndex.load(tmp_file.name   )
        os.unlink(tmp_file.name   )

        SCREAMING_SNAKE_CASE_          =  np.zeros(5  ,     dtype=np.floataa   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search(__magic_name__   )
        self.assertGreater(scores[0]  ,     0   )
        self.assertEqual(indices[0]  ,     1   )



@require_faiss
def      a__ (		__UpperCamelCase				):
      import faiss

      SCREAMING_SNAKE_CASE_          =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT				)
      index.add_vectors(np.eye(5     ,      dtype=np.floataa				)				)

      SCREAMING_SNAKE_CASE_          =  "index.faiss"
      SCREAMING_SNAKE_CASE_          =  F'''mock://{index_name}'''
      index.save(__UpperCamelCase     ,      storage_options=mockfs.storage_options				)
      SCREAMING_SNAKE_CASE_          =  FaissIndex.load(__UpperCamelCase     ,      storage_options=mockfs.storage_options				)

      SCREAMING_SNAKE_CASE_          =  np.zeros(5     ,      dtype=np.floataa				)
      SCREAMING_SNAKE_CASE_          =  1
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search(__UpperCamelCase				)
      assert scores[0] > 0
      assert indices[0] == 1





@require_elasticsearch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       Any   )  ->	Tuple:
        from elasticsearch import Elasticsearch

        with patch("elasticsearch.Elasticsearch.search"   ) as mocked_search, patch(
            "elasticsearch.client.IndicesClient.create"   ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"   ) as mocked_bulk:
              SCREAMING_SNAKE_CASE_          =  Elasticsearch()
              SCREAMING_SNAKE_CASE_          =  {"acknowledged": True}
              SCREAMING_SNAKE_CASE_          =  ElasticSearchIndex(es_client=__magic_name__   )
              mocked_bulk.return_value([(True, None)] * 3   )
              index.add_documents(["foo", "bar", "foobar"]   )

              # single query
              SCREAMING_SNAKE_CASE_          =  "foo"
              SCREAMING_SNAKE_CASE_          =  {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search(__magic_name__   )
              self.assertEqual(scores[0]  ,     1   )
              self.assertEqual(indices[0]  ,     0   )

              # single query with timeout
              SCREAMING_SNAKE_CASE_          =  "foo"
              SCREAMING_SNAKE_CASE_          =  {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search(__magic_name__  ,     request_timeout=30   )
              self.assertEqual(scores[0]  ,     1   )
              self.assertEqual(indices[0]  ,     0   )

              # batched queries
              SCREAMING_SNAKE_CASE_          =  ["foo", "bar", "foobar"]
              SCREAMING_SNAKE_CASE_          =  {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search_batch(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  [scores[0] for scores in total_scores]
              SCREAMING_SNAKE_CASE_          =  [indices[0] for indices in total_indices]
              self.assertGreater(np.min(__magic_name__   )  ,     0   )
              self.assertListEqual([1, 1, 1]  ,     __magic_name__   )

              # batched queries with timeout
              SCREAMING_SNAKE_CASE_          =  ["foo", "bar", "foobar"]
              SCREAMING_SNAKE_CASE_          =  {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  index.search_batch(__magic_name__  ,     request_timeout=30   )
              SCREAMING_SNAKE_CASE_          =  [scores[0] for scores in total_scores]
              SCREAMING_SNAKE_CASE_          =  [indices[0] for indices in total_indices]
              self.assertGreater(np.min(__magic_name__   )  ,     0   )
              self.assertListEqual([1, 1, 1]  ,     __magic_name__   )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


