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

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil


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







  def       __A   (							self       :				List[Any]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([1, 2, 3]      )      )
        self.assertEqual(arr.type     ,   pa.intaa()      )







  def       __A   (							self       :				Optional[int]      )     ->		Union[str, Any]:
        with self.assertRaises(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([1, 2, 3]      )     ,   type=pa.intaa()      )







  def       __A   (							self       :				int      )     ->		Any:
        with self.assertRaises(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([1, 2, 3]     ,   try_type=Value("bool"      )     ,   type=Value("int64"      )      )      )







  def       __A   (							self       :				str      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([1, 2, 3]     ,   type=Value("int32"      )      )      )
        self.assertEqual(arr.type     ,   pa.intaa()      )







  def       __A   (							self       :				int      )     ->		str:
        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
              SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence(["foo", "bar"]     ,   type=Value("int64"      )      )      )







  def       __A   (							self       :				List[str]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([1, 2, 3]     ,   try_type=Value("int32"      )      )      )
        self.assertEqual(arr.type     ,   pa.intaa()      )







  def       __A   (							self       :				Any      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence(["foo", "bar"]     ,   try_type=Value("int64"      )      )      )
        self.assertEqual(arr.type     ,   pa.string()      )







  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([[[1, 2, 3]]]     ,   type=ArrayaD((1, 3)     ,   "int64"      )      )      )
        self.assertEqual(arr.type     ,   ArrayaDExtensionType((1, 3)     ,   "int64"      )      )







  def       __A   (							self       :				Any      )     ->		Union[str, Any]:
        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
              SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence(["foo", "bar"]     ,   type=ArrayaD((1, 3)     ,   "int64"      )      )      )







  def       __A   (							self       :				str      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([[[1, 2, 3]]]     ,   try_type=ArrayaD((1, 3)     ,   "int64"      )      )      )
        self.assertEqual(arr.type     ,   ArrayaDExtensionType((1, 3)     ,   "int64"      )      )







  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence(["foo", "bar"]     ,   try_type=ArrayaD((1, 3)     ,   "int64"      )      )      )
        self.assertEqual(arr.type     ,   pa.string()      )







  @require_pil
  def       __A   (							self       :				int      )     ->		Any:
        import PIL.Image

        SCREAMING_SNAKE_CASE_      =     PIL.Image.fromarray(np.arange(10     ,   dtype=np.uinta      ).reshape(2     ,   5      )      )
        with patch(
            "datasets.arrow_writer.cast_to_python_objects"     ,   side_effect=__magic_name__      ) as mock_cast_to_python_objects:
              SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]     ,   type=Image()      )      )
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mock_cast_to_python_objects.call_args_list[-1]
              self.assertIn("optimize_list_casting"     ,   __magic_name__      )
              self.assertFalse(kwargs["optimize_list_casting"]      )



def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferReader(__UpperCamelCase ) if isinstance(__UpperCamelCase   ,					pa.Buffer ) else pa.memory_map(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     pa.ipc.open_stream(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     f.read_all()
      assert len(pa_table.to_batches() ) == expected_num_chunks
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
      del pa_table


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 1, 1_0] )
@pytest.mark.parametrize(
    "fields"   ,					[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      SCREAMING_SNAKE_CASE_      =     pa.schema(__UpperCamelCase ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase   ,					schema=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase ) as writer:
            writer.write({"col_1": "foo", "col_2": 1} )
            writer.write({"col_1": "bar", "col_2": 2} )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            SCREAMING_SNAKE_CASE_      =     {"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase   ,					metadata=writer._schema.metadata )
      _check_output(output.getvalue()   ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      SCREAMING_SNAKE_CASE_      =     Features({"labels": ClassLabel(names=["neg", "pos"] )} )
      with ArrowWriter(stream=__UpperCamelCase   ,					features=__UpperCamelCase ) as writer:
            writer.write({"labels": 0} )
            writer.write({"labels": 1} )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert writer._schema == features.arrow_schema
      assert writer._schema.metadata == features.arrow_schema.metadata
      SCREAMING_SNAKE_CASE_      =     pa.BufferReader(output.getvalue() )
      SCREAMING_SNAKE_CASE_      =     pa.ipc.open_stream(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     f.read_all()
      SCREAMING_SNAKE_CASE_      =     pa_table.schema
      assert pa_table.num_rows == 2
      assert schema == features.arrow_schema
      assert schema.metadata == features.arrow_schema.metadata
      assert features == Features.from_arrow_schema(__UpperCamelCase )


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 1, 1_0] )
def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      with ArrowWriter(
          stream=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase   ,					hash_salt="split_name"   ,					check_duplicates=__UpperCamelCase   ,					) as writer:
            with pytest.raises(__UpperCamelCase ):
                  writer.write({"col_1": "foo", "col_2": 1}   ,					key=[1, 2] )
                  SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 2, 1_0] )
def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      with ArrowWriter(
          stream=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase   ,					hash_salt="split_name"   ,					check_duplicates=__UpperCamelCase   ,					) as writer:
            with pytest.raises(__UpperCamelCase ):
                  writer.write({"col_1": "foo", "col_2": 1}   ,					key=1_0 )
                  writer.write({"col_1": "bar", "col_2": 2}   ,					key=1_0 )
                  SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 2, 1_0] )
def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      with ArrowWriter(
          stream=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase   ,					hash_salt="split_name"   ,					check_duplicates=__UpperCamelCase   ,					) as writer:
            writer.write({"col_1": "foo", "col_2": 1}   ,					key=1 )
            writer.write({"col_1": "bar", "col_2": 2}   ,					key=2 )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      _check_output(output.getvalue()   ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 1, 1_0] )
@pytest.mark.parametrize(
    "fields"   ,					[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      SCREAMING_SNAKE_CASE_      =     pa.schema(__UpperCamelCase ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase   ,					schema=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase ) as writer:
            writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]} )
            writer.write_batch({"col_1": [], "col_2": []} )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            SCREAMING_SNAKE_CASE_      =     {"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase   ,					metadata=writer._schema.metadata )
      _check_output(output.getvalue()   ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 1, 1_0] )
@pytest.mark.parametrize(
    "fields"   ,					[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      SCREAMING_SNAKE_CASE_      =     pa.schema(__UpperCamelCase ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase   ,					schema=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase ) as writer:
            writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]} ) )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            SCREAMING_SNAKE_CASE_      =     {"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase   ,					metadata=writer._schema.metadata )
      _check_output(output.getvalue()   ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )


@pytest.mark.parametrize("writer_batch_size"   ,					[None, 1, 1_0] )
@pytest.mark.parametrize(
    "fields"   ,					[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      SCREAMING_SNAKE_CASE_      =     pa.schema(__UpperCamelCase ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase   ,					schema=__UpperCamelCase   ,					writer_batch_size=__UpperCamelCase ) as writer:
            writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]} ) )
            writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]} ) )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            SCREAMING_SNAKE_CASE_      =     {"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase   ,					metadata=writer._schema.metadata )
      _check_output(output.getvalue()   ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )


def    a__       (						):
      with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE_      =     {"col_1": pa.string(), "col_2": pa.intaa()}
            SCREAMING_SNAKE_CASE_      =     os.path.join(__UpperCamelCase   ,					"test.arrow" )
            with ArrowWriter(path=__UpperCamelCase   ,					schema=pa.schema(__UpperCamelCase ) ) as writer:
                  writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]} )
                  SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
            assert num_examples == 2
            assert num_bytes > 0
            assert writer._schema == pa.schema(__UpperCamelCase   ,					metadata=writer._schema.metadata )
            _check_output(__UpperCamelCase   ,					1 )


def    a__       (						__UpperCamelCase ):
      if pa.types.is_list(__UpperCamelCase ):
            return get_base_dtype(arr_type.value_type )
      else:
            return arr_type


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      if isinstance(lst[0]   ,					__UpperCamelCase ):
            change_first_primitive_element_in_list(lst[0]   ,					__UpperCamelCase )
      else:
            SCREAMING_SNAKE_CASE_      =     value


@pytest.mark.parametrize("optimized_int_type, expected_dtype"   ,					[(None, pa.intaa()), (Value("int32" ), pa.intaa())] )
@pytest.mark.parametrize("sequence"   ,					[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     pa.array(TypedSequence(__UpperCamelCase   ,					optimized_int_type=__UpperCamelCase ) )
      assert get_base_dtype(arr.type ) == expected_dtype


@pytest.mark.parametrize(
    "col, expected_dtype"   ,					[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]   ,					)
@pytest.mark.parametrize("sequence"   ,					[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      # in range
      SCREAMING_SNAKE_CASE_      =     pa.array(OptimizedTypedSequence(__UpperCamelCase   ,					col=__UpperCamelCase ) )
      assert get_base_dtype(arr.type ) == expected_dtype

      # not in range
      if col != "other":
            # avoids errors due to in-place modifications
            SCREAMING_SNAKE_CASE_      =     copy.deepcopy(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
            change_first_primitive_element_in_list(__UpperCamelCase   ,					__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     pa.array(OptimizedTypedSequence(__UpperCamelCase   ,					col=__UpperCamelCase ) )
            assert get_base_dtype(arr.type ) == pa.intaa()


@pytest.mark.parametrize("raise_exception"   ,					[False, True] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     str(tmp_path / "dataset-train.arrow" )
      try:
            with ArrowWriter(path=__UpperCamelCase ) as writer:
                  if raise_exception:
                        raise pa.lib.ArrowInvalid()
                  else:
                        writer.stream.close()
      except pa.lib.ArrowInvalid:
            pass
      finally:
            assert writer.stream.closed


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     "mock://dataset-train.arrow"
      with ArrowWriter(path=__UpperCamelCase   ,					storage_options=mockfs.storage_options ) as writer:
            assert isinstance(writer._fs   ,					type(__UpperCamelCase ) )
            assert writer._fs.storage_options == mockfs.storage_options
            writer.write({"col_1": "foo", "col_2": 1} )
            writer.write({"col_1": "bar", "col_2": 2} )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert mockfs.exists(__UpperCamelCase )


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      with ParquetWriter(stream=__UpperCamelCase ) as writer:
            writer.write({"col_1": "foo", "col_2": 1} )
            writer.write({"col_1": "bar", "col_2": 2} )
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      SCREAMING_SNAKE_CASE_      =     pa.BufferReader(output.getvalue() )
      SCREAMING_SNAKE_CASE_      =     pq.read_table(__UpperCamelCase )
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("embed_local_files"   ,					[False, True] )
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      import PIL.Image

      SCREAMING_SNAKE_CASE_      =     str(tmp_path / "test_image_rgb.jpg" )
      PIL.Image.fromarray(np.zeros((5, 5)   ,					dtype=np.uinta ) ).save(__UpperCamelCase   ,					format="png" )
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      with ParquetWriter(
          stream=__UpperCamelCase   ,					features=Features({"image": Image()} )   ,					embed_local_files=__UpperCamelCase ) as writer:
            writer.write({"image": image_path} )
            writer.finalize()
      SCREAMING_SNAKE_CASE_      =     pa.BufferReader(output.getvalue() )
      SCREAMING_SNAKE_CASE_      =     pq.read_table(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     pa_table.to_pydict()
      if embed_local_files:
            assert isinstance(out["image"][0]["path"]   ,					__UpperCamelCase )
            with open(__UpperCamelCase   ,					"rb" ) as f:
                  assert out["image"][0]["bytes"] == f.read()
      else:
            assert out["image"][0]["path"] == image_path
            assert out["image"][0]["bytes"] is None





def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     pa.schema([pa.field("col_1"   ,					pa.string()   ,					nullable=__UpperCamelCase )] )
      SCREAMING_SNAKE_CASE_      =     pa.BufferOutputStream()
      with ArrowWriter(stream=__UpperCamelCase ) as writer:
            writer._build_writer(inferred_schema=__UpperCamelCase )
            assert writer._schema == pa.schema([pa.field("col_1"   ,					pa.string() )] )



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  DebertaTokenizer
  lowerCamelCase__   =  True
  lowerCamelCase__   =  DebertaTokenizerFast







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        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(__magic_name__     ,   range(len(__magic_name__      )      )      )      )
        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     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as fp:
              fp.write(json.dumps(__magic_name__      ) + "\n"      )
        with open(self.merges_file     ,   "w"     ,   encoding="utf-8"      ) as fp:
              fp.write("\n".join(__magic_name__      )      )







  def       __A   (							self       :				str     ,   **__magic_name__       :				int      )     ->		Union[str, Any]:
        kwargs.update(self.special_tokens_map      )
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				List[Any]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        return input_text, output_text







  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        SCREAMING_SNAKE_CASE_      =     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE_      =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )







  def       __A   (							self       :				Optional[int]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     tokenizer("Hello"     ,   "World"      )
        SCREAMING_SNAKE_CASE_      =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["token_type_ids"]     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Any      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     self.tokenizer_class.from_pretrained("microsoft/deberta-base"      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.encode("sequence builders"     ,   add_special_tokens=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.encode("multi-sequence build"     ,   add_special_tokens=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.encode(
            "sequence builders"     ,   add_special_tokens=__magic_name__     ,   add_prefix_space=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.encode(
            "sequence builders"     ,   "multi-sequence build"     ,   add_special_tokens=__magic_name__     ,   add_prefix_space=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.build_inputs_with_special_tokens(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.build_inputs_with_special_tokens(__magic_name__     ,   __magic_name__      )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







  @slow
  def       __A   (							self       :				Tuple      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     [self.tokenizer_class]
        if self.test_rust_tokenizer:
              tokenizer_classes.append(self.rust_tokenizer_class      )

        for tokenizer_class in tokenizer_classes:
              SCREAMING_SNAKE_CASE_      =     tokenizer_class.from_pretrained("microsoft/deberta-base"      )

              SCREAMING_SNAKE_CASE_      =     [
                  "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                  "ALBERT incorporates two parameter reduction techniques",
                  "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                  " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                  " vocabulary embedding.",
              ]

              SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   padding=__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     [tokenizer.decode(__magic_name__     ,   skip_special_tokens=__magic_name__      ) for seq in encoding["input_ids"]]

              # fmt: off
              SCREAMING_SNAKE_CASE_      =     {
                  "input_ids": [
                      [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                  ],
                  "token_type_ids": [
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                  ],
                  "attention_mask": [
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                  ]
              }
              # fmt: on

              SCREAMING_SNAKE_CASE_      =     [
                  "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                  "ALBERT incorporates two parameter reduction techniques",
                  "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                  " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                  " vocabulary embedding.",
              ]

              self.assertDictEqual(encoding.data     ,   __magic_name__      )

              for expected, decoded in zip(__magic_name__     ,   __magic_name__      ):
                    self.assertEqual(__magic_name__     ,   __magic_name__      )


