

"""simple docstring"""





import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a							=							get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     lowercase					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : str       		=     XLNetTokenizer
 __UpperCAmelCase     : Optional[Any]       		=     XLNetTokenizerFast
 __UpperCAmelCase     : int       		=     True
 __UpperCAmelCase     : Union[str, Any]       		=     True


 def  _lowercase			(      self							:      Union[str, Any]     ):
    super().setUp()

    # We have a SentencePiece fixture for testing
    __lowercase								=						XLNetTokenizer(UpperCAmelCase__, keep_accents=UpperCAmelCase__     )
    tokenizer.sanitize_special_tokens()
    tokenizer.save_pretrained(self.tmpdirname     )


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						"<s>"
    __lowercase								=						1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__     ), UpperCAmelCase__     )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__     ), UpperCAmelCase__     )


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						list(self.get_tokenizer().get_vocab().keys()     )

    self.assertEqual(vocab_keys[0], "<unk>"     )
    self.assertEqual(vocab_keys[1], "<s>"     )
    self.assertEqual(vocab_keys[-1], "<eod>"     )
    self.assertEqual(len(UpperCAmelCase__     ), 1_0_0_6     )


 def  _lowercase			(      self							:      List[str]     ):
    self.assertEqual(self.get_tokenizer().vocab_size, 1_0_0_0     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						XLNetTokenizer(UpperCAmelCase__, keep_accents=UpperCAmelCase__     )

    __lowercase								=						tokenizer.tokenize("This is a test"     )
    self.assertListEqual(UpperCAmelCase__, ["▁This", "▁is", "▁a", "▁t", "est"]     )

    self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__     ), [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]     )

    __lowercase								=						tokenizer.tokenize("I was born in 92000, and this is falsé."     )
    self.assertListEqual(
        UpperCAmelCase__, [
            SPIECE_UNDERLINE + "I",
            SPIECE_UNDERLINE + "was",
            SPIECE_UNDERLINE + "b",
            "or",
            "n",
            SPIECE_UNDERLINE + "in",
            SPIECE_UNDERLINE + "",
            "9",
            "2",
            "0",
            "0",
            "0",
            ",",
            SPIECE_UNDERLINE + "and",
            SPIECE_UNDERLINE + "this",
            SPIECE_UNDERLINE + "is",
            SPIECE_UNDERLINE + "f",
            "al",
            "s",
            "é",
            ".",
        ], )
    __lowercase								=						tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )
    self.assertListEqual(UpperCAmelCase__, [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]     )

    __lowercase								=						tokenizer.convert_ids_to_tokens(UpperCAmelCase__     )
    self.assertListEqual(
        UpperCAmelCase__, [
            SPIECE_UNDERLINE + "I",
            SPIECE_UNDERLINE + "was",
            SPIECE_UNDERLINE + "b",
            "or",
            "n",
            SPIECE_UNDERLINE + "in",
            SPIECE_UNDERLINE + "",
            "<unk>",
            "2",
            "0",
            "0",
            "0",
            ",",
            SPIECE_UNDERLINE + "and",
            SPIECE_UNDERLINE + "this",
            SPIECE_UNDERLINE + "is",
            SPIECE_UNDERLINE + "f",
            "al",
            "s",
            "<unk>",
            ".",
        ], )


 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						XLNetTokenizer(UpperCAmelCase__, do_lower_case=UpperCAmelCase__     )
    __lowercase								=						tokenizer.tokenize("I was born in 92000, and this is falsé."     )
    self.assertListEqual(
        UpperCAmelCase__, [
            SPIECE_UNDERLINE + "",
            "i",
            SPIECE_UNDERLINE + "was",
            SPIECE_UNDERLINE + "b",
            "or",
            "n",
            SPIECE_UNDERLINE + "in",
            SPIECE_UNDERLINE + "",
            "9",
            "2",
            "0",
            "0",
            "0",
            ",",
            SPIECE_UNDERLINE + "and",
            SPIECE_UNDERLINE + "this",
            SPIECE_UNDERLINE + "is",
            SPIECE_UNDERLINE + "f",
            "al",
            "se",
            ".",
        ], )
    self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     ), ["▁he", "ll", "o"]     )


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						XLNetTokenizer(UpperCAmelCase__, do_lower_case=UpperCAmelCase__     )
    __lowercase								=						tokenizer.tokenize("I was born in 92000, and this is falsé."     )
    self.assertListEqual(
        UpperCAmelCase__, [
            SPIECE_UNDERLINE + "I",
            SPIECE_UNDERLINE + "was",
            SPIECE_UNDERLINE + "b",
            "or",
            "n",
            SPIECE_UNDERLINE + "in",
            SPIECE_UNDERLINE + "",
            "9",
            "2",
            "0",
            "0",
            "0",
            ",",
            SPIECE_UNDERLINE + "and",
            SPIECE_UNDERLINE + "this",
            SPIECE_UNDERLINE + "is",
            SPIECE_UNDERLINE + "f",
            "al",
            "se",
            ".",
        ], )


 @slow
 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						XLNetTokenizer.from_pretrained("xlnet-base-cased"     )

    __lowercase								=						tokenizer.encode("sequence builders", add_special_tokens=UpperCAmelCase__     )
    __lowercase								=						tokenizer.encode("multi-sequence build", add_special_tokens=UpperCAmelCase__     )

    __lowercase								=						tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__     )
    __lowercase								=						tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__, UpperCAmelCase__     )

    assert encoded_sentence == text + [4, 3]
    assert encoded_pair == text + [4] + text_a + [4, 3]



 @slow
 def  _lowercase			(      self							:      Union[str, Any]     ):
    # fmt: off
    __lowercase								=						{"input_ids": [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], "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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], "attention_mask": [[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, 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, 1, 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, 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, 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], [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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=UpperCAmelCase__, model_name="xlnet-base-cased", revision="c841166438c31ec7ca9a106dee7bb312b73ae511", )




"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a							=							logging.get_logger(__name__)




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     ["pixel_values"]


 def __init__(      self							:      int, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Dict[str, int]] = None, UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BILINEAR, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Union[int, float] = 1 / 2_5_5, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, **UpperCAmelCase__							:      str, ):
    super().__init__(**UpperCAmelCase__     )
    __lowercase								=						size if size is not None else {"shortest_edge": 2_5_6}
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						resample
    __lowercase								=						do_center_crop
    __lowercase								=						crop_size
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __lowercase								=						do_normalize
    __lowercase								=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowercase								=						image_std if image_std is not None else IMAGENET_STANDARD_STD


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BICUBIC, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
    __lowercase								=						get_resize_output_image_size(UpperCAmelCase__, size=size["shortest_edge"], default_to_square=UpperCAmelCase__     )
    return resize(UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    return center_crop(UpperCAmelCase__, size=(size["height"], size["width"]), data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      float, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Union[str, Any]     ):
    return rescale(UpperCAmelCase__, scale=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      int, ):
    return normalize(UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      ImageInput, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      PILImageResampling = None, UpperCAmelCase__							:      bool = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[float] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[str, TensorType]] = None, UpperCAmelCase__							:      Union[str, ChannelDimension] = ChannelDimension.FIRST, **UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						do_resize if do_resize is not None else self.do_resize
    __lowercase								=						size if size is not None else self.size
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						resample if resample is not None else self.resample
    __lowercase								=						do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowercase								=						crop_size if crop_size is not None else self.crop_size
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_rescale if do_rescale is not None else self.do_rescale
    __lowercase								=						rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
    __lowercase								=						image_mean if image_mean is not None else self.image_mean
    __lowercase								=						image_std if image_std is not None else self.image_std

    __lowercase								=						make_list_of_images(UpperCAmelCase__     )

    if not valid_images(UpperCAmelCase__     ):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."     )

    if do_resize and size is None:
       raise ValueError("Size must be specified if do_resize is True."     )

    if do_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop is True."     )

    if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."     )

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."     )

    # All transformations expect numpy arrays.
    __lowercase								=						[to_numpy_array(UpperCAmelCase__     ) for image in images]

    if do_resize:
       __lowercase								=						[self.resize(image=UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__     ) for image in images]

    if do_center_crop:
       __lowercase								=						[self.center_crop(image=UpperCAmelCase__, size=UpperCAmelCase__     ) for image in images]

    if do_rescale:
       __lowercase								=						[self.rescale(image=UpperCAmelCase__, scale=UpperCAmelCase__     ) for image in images]

    if do_normalize:
       __lowercase								=						[self.normalize(image=UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__     ) for image in images]

    __lowercase								=						[to_channel_dimension_format(UpperCAmelCase__, UpperCAmelCase__     ) for image in images]

    __lowercase								=						{"pixel_values": images}
    return BatchFeature(data=UpperCAmelCase__, tensor_type=UpperCAmelCase__     )




