



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		(       UpperCAmelCase__	):

   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    a						=		5

    # Realm tok
    a						=		[
        "[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",
    ]
    a						=		os.path.join(self.tmpdirname							,   "realm_tokenizer"							)
    os.makedirs(__lowerCAmelCase							,   exist_ok=__lowerCAmelCase							)
    a						=		os.path.join(__lowerCAmelCase							,   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]							)							)

    a						=		os.path.join(self.tmpdirname							,   "realm_block_records"							)
    os.makedirs(__lowerCAmelCase							,   exist_ok=__lowerCAmelCase							)

   def 		A							(      self			:       List[str]							)       ->				RealmTokenizer:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    shutil.rmtree(self.tmpdirname							)

   def 		A							(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






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

   def 		A							(      self			:       str							)       ->				Optional[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		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=__lowerCAmelCase							,   )
    return block_records

   def 		A							(      self			:       Any							)       ->				Dict:


    """simple docstring"""






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

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


    """simple docstring"""






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

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

    a		,  a		,  a		,  a						=		retriever(
        __lowerCAmelCase							,   __lowerCAmelCase							,   answer_ids=__lowerCAmelCase							,   max_length=__lowerCAmelCase							,   return_tensors="np"							)

    self.assertEqual(len(__lowerCAmelCase							)							,   2							)
    self.assertEqual(len(__lowerCAmelCase							)							,   2							)
    self.assertEqual(len(__lowerCAmelCase							)							,   2							)
    self.assertEqual(concat_inputs.input_ids.shape							,   (2, 10)							)
    self.assertEqual(concat_inputs.attention_mask.shape							,   (2, 10)							)
    self.assertEqual(concat_inputs.token_type_ids.shape							,   (2, 10)							)
    self.assertEqual(concat_inputs.special_tokens_mask.shape							,   (2, 10)							)
    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 		A							(      self			:       int							)       ->				int:


    """simple docstring"""






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

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

    a		,  a		,  a		,  a						=		retriever(
        __lowerCAmelCase							,   __lowerCAmelCase							,   answer_ids=__lowerCAmelCase							,   max_length=__lowerCAmelCase							,   return_tensors="np"							)

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




   def 		A							(      self			:       Union[str, Any]							)       ->				Tuple:


    """simple docstring"""






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

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

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






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(UpperCAmelCase__	),   '''Tatoeba directory does not exist.'''	)
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    self.resolver.convert_models(["heb-eng"]							)




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.resolver.write_model_card("opus-mt-he-en"							,   dry_run=__lowerCAmelCase							)
    assert mmeta["long_pair"] == "heb-eng"

