"""simple docstring"""




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 					A							(    UpperCAmelCase_					):


   def       lowercase_				(self					:						Union[str, Any]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  tempfile.mkdtemp()
         UpperCAmelCase__								=  5

         # Realm tok
         UpperCAmelCase__								=  [
             "[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__								=  os.path.join(self.tmpdirname	,  "realm_tokenizer"		)
         os.makedirs(__UpperCAmelCase	,  exist_ok=__UpperCAmelCase		)
         UpperCAmelCase__								=  os.path.join(__UpperCAmelCase	,  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__								=  os.path.join(self.tmpdirname	,  "realm_block_records"		)
         os.makedirs(__UpperCAmelCase	,  exist_ok=__UpperCAmelCase		)


   def       lowercase_				(self					:						int		)						->    RealmTokenizer:
         """simple docstring"""



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


   def       lowercase_				(self					:						Dict		)						->    List[Any]:
         """simple docstring"""



         shutil.rmtree(self.tmpdirname		)


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  RealmConfig(num_block_records=self.num_block_records		)
         return config


   def       lowercase_				(self					:						Optional[int]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "question": ["foo", "bar"],
                 "answers": [["Foo", "Bar"], ["Bar"]],
             }		)
         return dataset


   def       lowercase_				(self					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  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=__UpperCAmelCase	,  )
         return block_records


   def       lowercase_				(self					:						Optional[int]		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  RealmRetriever(
             block_records=self.get_dummy_block_records()	,  tokenizer=self.get_tokenizer()	,  )
         return retriever


   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_config()
         UpperCAmelCase__								=  self.get_dummy_retriever()
         UpperCAmelCase__								=  retriever.tokenizer

         UpperCAmelCase__								=  np.array([0, 3]	,  dtype="long"		)
         UpperCAmelCase__								=  tokenizer(["Test question"]		).input_ids
         UpperCAmelCase__								=  tokenizer(
             ["the fourth"]	,  add_special_tokens=__UpperCAmelCase	,  return_token_type_ids=__UpperCAmelCase	,  return_attention_mask=__UpperCAmelCase	,  ).input_ids
         UpperCAmelCase__								=  config.reader_seq_len

         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  retriever(
             __UpperCAmelCase	,  __UpperCAmelCase	,  answer_ids=__UpperCAmelCase	,  max_length=__UpperCAmelCase	,  return_tensors="np"		)

         self.assertEqual(len(__UpperCAmelCase		)	,  2		)
         self.assertEqual(len(__UpperCAmelCase		)	,  2		)
         self.assertEqual(len(__UpperCAmelCase		)	,  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       lowercase_				(self					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_config()
         UpperCAmelCase__								=  self.get_dummy_retriever()
         UpperCAmelCase__								=  retriever.tokenizer

         UpperCAmelCase__								=  np.array([0, 3, 5]	,  dtype="long"		)
         UpperCAmelCase__								=  tokenizer(["Test question"]		).input_ids
         UpperCAmelCase__								=  tokenizer(
             ["the fourth", "longer longer"]	,  add_special_tokens=__UpperCAmelCase	,  return_token_type_ids=__UpperCAmelCase	,  return_attention_mask=__UpperCAmelCase	,  ).input_ids
         UpperCAmelCase__								=  config.reader_seq_len

         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  retriever(
             __UpperCAmelCase	,  __UpperCAmelCase	,  answer_ids=__UpperCAmelCase	,  max_length=__UpperCAmelCase	,  return_tensors="np"		)

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





   def       lowercase_				(self					:						List[str]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname	,  "realm_block_records"		)		)

         # Test local path
         UpperCAmelCase__								=  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__								=  os.path.join(
                   os.path.join(self.tmpdirname	,  "realm_block_records"		)	,  _REALM_BLOCK_RECORDS_FILENAME		)
               UpperCAmelCase__								=  RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"		)

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




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'facebook/bart-large-mnli'
   __UpperCAmelCase			:			Optional[Any]              =      (
       'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
       'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
       'It returns the most likely label in the list of provided `labels` for the input text.'
   )
   __UpperCAmelCase			:			Optional[int]              =      'text_classifier'
   __UpperCAmelCase			:			int              =      AutoTokenizer
   __UpperCAmelCase			:			Dict              =      AutoModelForSequenceClassification

   __UpperCAmelCase			:			int              =      ['text', ['text']]
   __UpperCAmelCase			:			Optional[int]              =      ['text']


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         super().setup()
         UpperCAmelCase__								=  self.model.config
         UpperCAmelCase__								=  -1
         for idx, label in config.idalabel.items():
               if label.lower().startswith("entail"		):
                     UpperCAmelCase__								=  int(__UpperCAmelCase		)
         if self.entailment_id == -1:
               raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  labels
         return self.pre_processor(
             [text] * len(__UpperCAmelCase		)	,  [f"""This example is {label}""" for label in labels]	,  return_tensors="pt"	,  padding="max_length"	,  )





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  outputs.logits
         UpperCAmelCase__								=  torch.argmax(logits[:, 2]		).item()
         return self._labels[label_id]



