

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:
       UpperCamelCase__     :					Any								=			None

if is_ray_available():
       import ray  # noqa: E402 # isort:skip
       from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
       UpperCamelCase__     :					Dict								=			None
       UpperCamelCase__     :					Union[str, Any]								=			None
       UpperCamelCase__     :					List[Any]								=			None


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int				):





    """simple docstring"""


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





@require_distributed_retrieval
class 					__snake_case     (			lowercase_				):

  def   lowerCAmelCase__   (    self):
      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(_lowercase  ,						exist_ok=_lowercase)
      SCREAMING_SNAKE_CASE_													=  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
      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(_lowercase  ,						range(len(_lowercase))))
      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(_lowercase  ,						exist_ok=_lowercase)
      SCREAMING_SNAKE_CASE_													=  os.path.join(_lowercase  ,						BART_VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self):
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'dpr_tokenizer'))

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

  def   lowerCAmelCase__   (    self):
      shutil.rmtree(self.tmpdirname)

  def   lowerCAmelCase__   (    self):
      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  ,						_A  ,						_A=12345):
      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(
              _lowercase  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						)
          if init_retrieval:
              retriever.init_retrieval(_lowercase)
      return retriever

  def   lowerCAmelCase__   (    self  ,						_A):
      ray.init(local_mode=_lowercase)
      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(_lowercase)
      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(
              _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   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A=12345):
      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(
              _lowercase  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						)
      else:
          SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self  ,						_A  ,						_A):
      ray.init(local_mode=_lowercase)
      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(_lowercase)
      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(
              _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:
          SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self):
      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=_lowercase)  ,						_lowercase  ,						_lowercase)

  def   lowerCAmelCase__   (    self):
      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=_lowercase  ,						from_disk=_lowercase)  ,						_lowercase  ,						_lowercase  ,						)

  def   lowerCAmelCase__   (    self):
      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=_lowercase  ,						from_disk=_lowercase)  ,						_lowercase  ,						_lowercase  ,						)

  @require_ray
  def   lowerCAmelCase__   (    self):
      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=_lowercase)  ,						_lowercase  ,						_lowercase)
      ray.shutdown()

  @require_ray
  def   lowerCAmelCase__   (    self):
      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(_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   lowerCAmelCase__   (    self):
      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=_lowercase  ,						from_disk=_lowercase)  ,						_lowercase  ,						_lowercase)
      ray.shutdown()









from typing import TYPE_CHECKING

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


_snake_case				= {"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RegNetForImageClassification""",
      """RegNetModel""",
      """RegNetPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRegNetForImageClassification""",
      """TFRegNetModel""",
      """TFRegNetPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """FlaxRegNetForImageClassification""",
      """FlaxRegNetModel""",
      """FlaxRegNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






