import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer





class 		_lowercase						(					snake_case_       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					tempfile.mkdtemp()
  UpperCamelCase_					:						Tuple =					5

  # Realm tok
  UpperCamelCase_					:						int =					[
      "[UNK]",
      "[CLS]",
      "[SEP]",
      "[PAD]",
      "[MASK]",
      "test",
      "question",
      "this",
      "is",
      "the",
      "first",
      "second",
      "third",
      "fourth",
      "fifth",
      "record",
      "want",
      "##want",
      "##ed",
      "wa",
      "un",
      "runn",
      "##ing",
      ",",
      "low",
      "lowest",
  ]
  UpperCamelCase_					:						str =					os.path.join(self.tmpdirname   ,      'realm_tokenizer'			)
  os.makedirs(__A   ,      exist_ok=__A			)
  UpperCamelCase_					:						Optional[Any] =					os.path.join(__A   ,      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]			)			)

  UpperCamelCase_					:						Optional[int] =					os.path.join(self.tmpdirname   ,      'realm_block_records'			)
  os.makedirs(__A   ,      exist_ok=__A			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Union[str, Any]:

  """simple docstring"""





  return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,      'realm_tokenizer'			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Dict:

  """simple docstring"""





  shutil.rmtree(self.tmpdirname			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					RealmConfig(num_block_records=self.num_block_records			)
  return config

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					Dataset.from_dict(
      {
          'id': ['0', '1'],
          'question': ['foo', 'bar'],
          'answers': [['Foo', 'Bar'], ['Bar']],
      }			)
  return dataset

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					np.array(
      [
          B'This is the first record',
          B'This is the second record',
          B'This is the third record',
          B'This is the fourth record',
          B'This is the fifth record',
          B'This is a longer longer longer record',
      ]   ,      dtype=__A   ,      )
  return block_records

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					RealmRetriever(
      block_records=self.get_dummy_block_records()   ,      tokenizer=self.get_tokenizer()   ,      )
  return retriever

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.get_config()
  UpperCamelCase_					:						List[Any] =					self.get_dummy_retriever()
  UpperCamelCase_					:						List[Any] =					retriever.tokenizer

  UpperCamelCase_					:						Tuple =					np.array([0, 3]   ,      dtype='long'			)
  UpperCamelCase_					:						Union[str, Any] =					tokenizer(['Test question']			).input_ids
  UpperCamelCase_					:						Optional[Any] =					tokenizer(
      ['the fourth']   ,      add_special_tokens=__A   ,      return_token_type_ids=__A   ,      return_attention_mask=__A   ,      ).input_ids
  UpperCamelCase_					:						int =					config.reader_seq_len

  UpperCamelCase_					:						List[str] =					retriever(
      __A   ,      __A   ,      answer_ids=__A   ,      max_length=__A   ,      return_tensors='np'			)

  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(len(__A			)   ,      2			)
  self.assertEqual(concat_inputs.input_ids.shape   ,      (2, 1_0)			)
  self.assertEqual(concat_inputs.attention_mask.shape   ,      (2, 1_0)			)
  self.assertEqual(concat_inputs.token_type_ids.shape   ,      (2, 1_0)			)
  self.assertEqual(concat_inputs.special_tokens_mask.shape   ,      (2, 1_0)			)
  self.assertEqual(
      tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]			)   ,      ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']   ,      )
  self.assertEqual(
      tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]			)   ,      ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']   ,      )

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

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.get_config()
  UpperCamelCase_					:						Union[str, Any] =					self.get_dummy_retriever()
  UpperCamelCase_					:						Optional[Any] =					retriever.tokenizer

  UpperCamelCase_					:						str =					np.array([0, 3, 5]   ,      dtype='long'			)
  UpperCamelCase_					:						List[Any] =					tokenizer(['Test question']			).input_ids
  UpperCamelCase_					:						Dict =					tokenizer(
      ['the fourth', 'longer longer']   ,      add_special_tokens=__A   ,      return_token_type_ids=__A   ,      return_attention_mask=__A   ,      ).input_ids
  UpperCamelCase_					:						Any =					config.reader_seq_len

  UpperCamelCase_					:						Optional[int] =					retriever(
      __A   ,      __A   ,      answer_ids=__A   ,      max_length=__A   ,      return_tensors='np'			)

  self.assertEqual([False, True, True]   ,      __A			)
  self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,      __A			)
  self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,      __A			)


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

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.get_dummy_retriever()
  retriever.save_pretrained(os.path.join(self.tmpdirname   ,      'realm_block_records'			)			)

  # Test local path
  UpperCamelCase_					:						Optional[int] =					retriever.from_pretrained(os.path.join(self.tmpdirname   ,      'realm_block_records'			)			)
  self.assertEqual(retriever.block_records[0]   ,      B'This is the first record'			)

  # Test mocked remote path
  with patch('transformers.models.realm.retrieval_realm.hf_hub_download'			) as mock_hf_hub_download:
   UpperCamelCase_					:						List[Any] =					os.path.join(
       os.path.join(self.tmpdirname   ,      'realm_block_records'			)   ,      _REALM_BLOCK_RECORDS_FILENAME			)
   UpperCamelCase_					:						Optional[Any] =					RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa'			)

  self.assertEqual(retriever.block_records[0]   ,      B'This is the first record'			)







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case             =     logging.get_logger(__name__)
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





     warnings.warn(
         "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use DPTImageProcessor instead."						,				__A						,				)
     super().__init__(*__A						,				**__A )



