


import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__  :List[str]           =							get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
@require_tokenizers
class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  List[str]           =     ReformerTokenizer
		_a     :  List[str]           =     ReformerTokenizerFast
		_a     :  Union[str, Any]           =     True
		_a     :  Any           =     False
		_a     :  Tuple           =     True
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									super().setUp()

									_UpperCAmelCase					=      ReformerTokenizer(lowerCamelCase_      ,   keep_accents=lowerCamelCase_						)
									tokenizer.save_pretrained(self.tmpdirname						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      '<s>'
									_UpperCAmelCase					=      1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_						)      ,   lowerCamelCase_						)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_						)      ,   lowerCamelCase_						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]      ,   '<unk>'						)
									self.assertEqual(vocab_keys[1]      ,   '<s>'						)
									self.assertEqual(vocab_keys[-1]      ,   'j'						)
									self.assertEqual(len(lowerCamelCase_						)      ,   1000						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									self.assertEqual(self.get_tokenizer().vocab_size      ,   1000						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									if not self.test_rust_tokenizer:
																return

									_UpperCAmelCase					=      self.get_tokenizer()
									_UpperCAmelCase					=      self.get_rust_tokenizer()

									_UpperCAmelCase					=      'I was born in 92000, and this is falsé.'

									_UpperCAmelCase					=      tokenizer.tokenize(lowerCamelCase_						)
									_UpperCAmelCase					=      rust_tokenizer.tokenize(lowerCamelCase_						)
									self.assertListEqual(lowerCamelCase_      ,   lowerCamelCase_						)

									_UpperCAmelCase					=      tokenizer.encode(lowerCamelCase_      ,   add_special_tokens=lowerCamelCase_						)
									_UpperCAmelCase					=      rust_tokenizer.encode(lowerCamelCase_      ,   add_special_tokens=lowerCamelCase_						)
									self.assertListEqual(lowerCamelCase_      ,   lowerCamelCase_						)

									_UpperCAmelCase					=      self.get_rust_tokenizer()
									_UpperCAmelCase					=      tokenizer.encode(lowerCamelCase_						)
									_UpperCAmelCase					=      rust_tokenizer.encode(lowerCamelCase_						)
									self.assertListEqual(lowerCamelCase_      ,   lowerCamelCase_						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=15						)			->	Union[str, Any]:




									"""simple docstring"""





									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
																							_UpperCAmelCase					=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase_      ,   **lowerCamelCase_						)

																							# Simple input
																							_UpperCAmelCase					=      'This is a simple input'
																							_UpperCAmelCase					=      ['This is a simple input 1', 'This is a simple input 2']
																							_UpperCAmelCase					=      ('This is a simple input', 'This is a pair')
																							_UpperCAmelCase					=      [
																							    ('This is a simple input 1', 'This is a simple input 2'),
																							    ('This is a simple pair 1', 'This is a simple pair 2'),
																							]

																							# Simple input tests
																							self.assertRaises(lowerCamelCase_      ,   tokenizer_r.encode      ,   lowerCamelCase_      ,   max_length=lowerCamelCase_      ,   padding='max_length'						)

																							# Simple input
																							self.assertRaises(lowerCamelCase_      ,   tokenizer_r.encode_plus      ,   lowerCamelCase_      ,   max_length=lowerCamelCase_      ,   padding='max_length'						)

																							# Simple input
																							self.assertRaises(
																							    lowerCamelCase_      ,   tokenizer_r.batch_encode_plus      ,   lowerCamelCase_      ,   max_length=lowerCamelCase_      ,   padding='max_length'      ,   )

																							# Pair input
																							self.assertRaises(lowerCamelCase_      ,   tokenizer_r.encode      ,   lowerCamelCase_      ,   max_length=lowerCamelCase_      ,   padding='max_length'						)

																							# Pair input
																							self.assertRaises(lowerCamelCase_      ,   tokenizer_r.encode_plus      ,   lowerCamelCase_      ,   max_length=lowerCamelCase_      ,   padding='max_length'						)

																							# Pair input
																							self.assertRaises(
																							    lowerCamelCase_      ,   tokenizer_r.batch_encode_plus      ,   lowerCamelCase_      ,   max_length=lowerCamelCase_      ,   padding='max_length'      ,   )
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      ReformerTokenizer(lowerCamelCase_      ,   keep_accents=lowerCamelCase_						)

									_UpperCAmelCase					=      tokenizer.tokenize('This is a test'						)
									self.assertListEqual(lowerCamelCase_      ,   ['▁This', '▁is', '▁a', '▁t', 'est']						)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCamelCase_						)      ,   [285, 46, 10, 170, 382]      ,   )

									_UpperCAmelCase					=      tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
									self.assertListEqual(
									    lowerCamelCase_      ,   [
									        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',
									        '.',
									    ]      ,   )
									_UpperCAmelCase					=      tokenizer.convert_tokens_to_ids(lowerCamelCase_						)
									self.assertListEqual(
									    lowerCamelCase_      ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,   )

									_UpperCAmelCase					=      tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
									self.assertListEqual(
									    lowerCamelCase_      ,   [
									        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>',
									        '.',
									    ]      ,   )
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      'Hello World!'
									_UpperCAmelCase					=      [126, 32, 262, 152, 38, 72, 287]

									self.assertListEqual(lowerCamelCase_      ,   self.big_tokenizer.encode(lowerCamelCase_						)						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      (
									    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'
									    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
									)
									_UpperCAmelCase					=      [
									    108,
									    265,
									    24,
									    111,
									    4,
									    258,
									    156,
									    35,
									    28,
									    275,
									    3,
									    259,
									    297,
									    260,
									    84,
									    4,
									    35,
									    110,
									    44,
									    8,
									    259,
									    91,
									    268,
									    21,
									    11,
									    209,
									    274,
									    109,
									    266,
									    277,
									    117,
									    86,
									    93,
									    315,
									    258,
									    278,
									    258,
									    277,
									    258,
									    0,
									    258,
									    288,
									    258,
									    319,
									    258,
									    0,
									    258,
									    0,
									    258,
									    0,
									    258,
									    0,
									    258,
									    287,
									    258,
									    315,
									    258,
									    289,
									    258,
									    278,
									    99,
									    269,
									    266,
									    262,
									    8,
									    259,
									    241,
									    4,
									    217,
									    230,
									    268,
									    266,
									    55,
									    168,
									    106,
									    75,
									    193,
									    266,
									    223,
									    27,
									    49,
									    26,
									    282,
									    25,
									    264,
									    299,
									    19,
									    26,
									    0,
									    258,
									    277,
									    117,
									    86,
									    93,
									    176,
									    183,
									    270,
									    11,
									    262,
									    42,
									    61,
									    265,
									]

									self.assertListEqual(lowerCamelCase_      ,   self.big_tokenizer.encode(lowerCamelCase_						)						)
		@require_torch
		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									import torch

									from transformers import ReformerConfig, ReformerModel

									# Build sequence
									_UpperCAmelCase					=      list(self.big_tokenizer.get_vocab().keys()						)[:10]
									_UpperCAmelCase					=      ' '.join(lowerCamelCase_						)
									_UpperCAmelCase					=      self.big_tokenizer.encode_plus(lowerCamelCase_      ,   return_tensors='pt'						)
									_UpperCAmelCase					=      self.big_tokenizer.batch_encode_plus([sequence, sequence]      ,   return_tensors='pt'						)

									_UpperCAmelCase					=      ReformerConfig()
									# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
									_UpperCAmelCase					=      encoded_sequence['input_ids'].shape
									_UpperCAmelCase					=      ReformerModel(lowerCamelCase_						)

									# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
																model(**lowerCamelCase_						)
																model(**lowerCamelCase_						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '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]]}  # noqa: E501
									# fmt: on

									# This tokenizer does not know some characters like ")".
									# That is the reason why we use very simple texts here.
									# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
									_UpperCAmelCase					=      [
									    'This is a very simple sentence.',
									    'The quick brown fox jumps over the lazy dog.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCamelCase_      ,   model_name='google/reformer-crime-and-punishment'      ,   revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'      ,   padding=lowerCamelCase_      ,   sequences=lowerCamelCase_      ,   )




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, 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, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else IMAGENET_STANDARD_STD
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std

									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																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 or resample is None:
																raise ValueError('Size and resample 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.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

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

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

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

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

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
