
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConditionalDetrImageProcessor







class      A__							(     unittest.TestCase	):






				def __init__(						self					:      Tuple    ,     a					:      Tuple    ,     a					:      Union[str, Any]=7    ,     a					:      Optional[int]=3    ,     a					:      Dict=30    ,     a					:      List[str]=400    ,     a					:      Optional[Any]=True    ,     a					:      str=None    ,     a					:      Union[str, Any]=True    ,     a					:      Tuple=[0.5, 0.5, 0.5]    ,     a					:      Union[str, Any]=[0.5, 0.5, 0.5]    ,     a					:      Optional[int]=True    ,     a					:      Any=1 / 255    ,     a					:      str=True    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
							lowerCAmelCase__      :      int         =		parent
							lowerCAmelCase__      :      int         =		batch_size
							lowerCAmelCase__      :      Tuple         =		num_channels
							lowerCAmelCase__      :      Dict         =		min_resolution
							lowerCAmelCase__      :      List[str]         =		max_resolution
							lowerCAmelCase__      :      Optional[Any]         =		do_resize
							lowerCAmelCase__      :      List[str]         =		size
							lowerCAmelCase__      :      Optional[Any]         =		do_normalize
							lowerCAmelCase__      :      Tuple         =		image_mean
							lowerCAmelCase__      :      str         =		image_std
							lowerCAmelCase__      :      Union[str, Any]         =		do_rescale
							lowerCAmelCase__      :      Dict         =		rescale_factor
							lowerCAmelCase__      :      Tuple         =		do_pad






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Tuple    ,     a					:      Optional[Any]=False   ):






							'''simple docstring'''







							if not batched:
										lowerCAmelCase__      :      Optional[int]         =		image_inputs[0]
										if isinstance(a    ,     Image.Image   ):
													lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		image.size
										else:
													lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		image.shape[1], image.shape[2]
										if w < h:
													lowerCAmelCase__      :      Union[str, Any]         =		int(self.size['shortest_edge'] * h / w   )
													lowerCAmelCase__      :      List[str]         =		self.size['shortest_edge']
										elif w > h:
													lowerCAmelCase__      :      List[Any]         =		self.size['shortest_edge']
													lowerCAmelCase__      :      List[str]         =		int(self.size['shortest_edge'] * w / h   )
										else:
													lowerCAmelCase__      :      Union[str, Any]         =		self.size['shortest_edge']
													lowerCAmelCase__      :      Any         =		self.size['shortest_edge']

							else:
										lowerCAmelCase__      :      int         =		[]
										for image in image_inputs:
													lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[Any]         =		self.get_expected_values([image]   )
													expected_values.append((expected_height, expected_width)   )
										lowerCAmelCase__      :      int         =		max(a    ,     key=lambda a   : item[0]   )[0]
										lowerCAmelCase__      :      Dict         =		max(a    ,     key=lambda a   : item[1]   )[1]

							return expected_height, expected_width







@require_torch
@require_vision
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  ConditionalDetrImageProcessor if is_vision_available() else None






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		ConditionalDetrImageProcessingTester(self   )






				@property
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return self.image_processor_tester.prepare_image_processor_dict()






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(a    ,     'image_mean'   )   )
							self.assertTrue(hasattr(a    ,     'image_std'   )   )
							self.assertTrue(hasattr(a    ,     'do_normalize'   )   )
							self.assertTrue(hasattr(a    ,     'do_resize'   )   )
							self.assertTrue(hasattr(a    ,     'size'   )   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size    ,     {'shortest_edge': 18, 'longest_edge': 1_333}   )
							self.assertEqual(image_processor.do_pad    ,     a   )

							lowerCAmelCase__      :      str         =		self.image_processing_class.from_dict(
							    self.image_processor_dict    ,     size=42    ,     max_size=84    ,     pad_and_return_pixel_mask=a   )
							self.assertEqual(image_processor.size    ,     {'shortest_edge': 42, 'longest_edge': 84}   )
							self.assertEqual(image_processor.do_pad    ,     a   )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowerCAmelCase__      :      Any         =		prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=a   )
							for image in image_inputs:
										self.assertIsInstance(a    ,     Image.Image   )

							# Test not batched input
							lowerCAmelCase__      :      Optional[Any]         =		image_processing(image_inputs[0]    ,     return_tensors='pt'   ).pixel_values

							lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		self.image_processor_tester.get_expected_values(a   )

							self.assertEqual(
							    encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

							# Test batched
							lowerCAmelCase__      ,				lowerCAmelCase__      :      List[str]         =		self.image_processor_tester.get_expected_values(a    ,     batched=a   )

							lowerCAmelCase__      :      List[Any]         =		image_processing(a    ,     return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,     )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowerCAmelCase__      :      List[Any]         =		prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=a    ,     numpify=a   )
							for image in image_inputs:
										self.assertIsInstance(a    ,     np.ndarray   )

							# Test not batched input
							lowerCAmelCase__      :      int         =		image_processing(image_inputs[0]    ,     return_tensors='pt'   ).pixel_values

							lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		self.image_processor_tester.get_expected_values(a   )

							self.assertEqual(
							    encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

							# Test batched
							lowerCAmelCase__      :      Tuple         =		image_processing(a    ,     return_tensors='pt'   ).pixel_values

							lowerCAmelCase__      ,				lowerCAmelCase__      :      Dict         =		self.image_processor_tester.get_expected_values(a    ,     batched=a   )

							self.assertEqual(
							    encoded_images.shape    ,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,     )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowerCAmelCase__      :      int         =		prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=a    ,     torchify=a   )
							for image in image_inputs:
										self.assertIsInstance(a    ,     torch.Tensor   )

							# Test not batched input
							lowerCAmelCase__      :      Union[str, Any]         =		image_processing(image_inputs[0]    ,     return_tensors='pt'   ).pixel_values

							lowerCAmelCase__      ,				lowerCAmelCase__      :      Tuple         =		self.image_processor_tester.get_expected_values(a   )

							self.assertEqual(
							    encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

							# Test batched
							lowerCAmelCase__      :      str         =		image_processing(a    ,     return_tensors='pt'   ).pixel_values

							lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		self.image_processor_tester.get_expected_values(a    ,     batched=a   )

							self.assertEqual(
							    encoded_images.shape    ,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,     )






				@slow
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,     'r'   ) as f:
										lowerCAmelCase__      :      Any         =		json.loads(f.read()   )

							lowerCAmelCase__      :      Union[str, Any]         =		{'image_id': 39_769, 'annotations': target}

							# encode them
							lowerCAmelCase__      :      str         =		ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'   )
							lowerCAmelCase__      :      Tuple         =		image_processing(images=a    ,     annotations=a    ,     return_tensors='pt'   )

							# verify pixel values
							lowerCAmelCase__      :      Optional[Any]         =		torch.Size([1, 3, 800, 1_066]   )
							self.assertEqual(encoding['pixel_values'].shape    ,     a   )

							lowerCAmelCase__      :      int         =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]   )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,     a    ,     atol=1E-4   )   )

							# verify area
							lowerCAmelCase__      :      Any         =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,     a   )   )
							# verify boxes
							lowerCAmelCase__      :      List[Any]         =		torch.Size([6, 4]   )
							self.assertEqual(encoding['labels'][0]['boxes'].shape    ,     a   )
							lowerCAmelCase__      :      List[Any]         =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,     a    ,     atol=1E-3   )   )
							# verify image_id
							lowerCAmelCase__      :      Dict         =		torch.tensor([39_769]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,     a   )   )
							# verify is_crowd
							lowerCAmelCase__      :      str         =		torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,     a   )   )
							# verify class_labels
							lowerCAmelCase__      :      List[str]         =		torch.tensor([75, 75, 63, 65, 17, 17]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,     a   )   )
							# verify orig_size
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([480, 640]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,     a   )   )
							# verify size
							lowerCAmelCase__      :      Dict         =		torch.tensor([800, 1_066]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,     a   )   )






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,     'r'   ) as f:
										lowerCAmelCase__      :      Dict         =		json.loads(f.read()   )

							lowerCAmelCase__      :      int         =		{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

							lowerCAmelCase__      :      Any         =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

							# encode them
							lowerCAmelCase__      :      Optional[int]         =		ConditionalDetrImageProcessor(format='coco_panoptic'   )
							lowerCAmelCase__      :      str         =		image_processing(images=a    ,     annotations=a    ,     masks_path=a    ,     return_tensors='pt'   )

							# verify pixel values
							lowerCAmelCase__      :      List[str]         =		torch.Size([1, 3, 800, 1_066]   )
							self.assertEqual(encoding['pixel_values'].shape    ,     a   )

							lowerCAmelCase__      :      List[str]         =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]   )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,     a    ,     atol=1E-4   )   )

							# verify area
							lowerCAmelCase__      :      List[str]         =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,     a   )   )
							# verify boxes
							lowerCAmelCase__      :      Any         =		torch.Size([6, 4]   )
							self.assertEqual(encoding['labels'][0]['boxes'].shape    ,     a   )
							lowerCAmelCase__      :      Union[str, Any]         =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,     a    ,     atol=1E-3   )   )
							# verify image_id
							lowerCAmelCase__      :      Tuple         =		torch.tensor([39_769]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,     a   )   )
							# verify is_crowd
							lowerCAmelCase__      :      Optional[Any]         =		torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,     a   )   )
							# verify class_labels
							lowerCAmelCase__      :      Any         =		torch.tensor([17, 17, 63, 75, 75, 93]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,     a   )   )
							# verify masks
							lowerCAmelCase__      :      Union[str, Any]         =		822_873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,     a   )
							# verify orig_size
							lowerCAmelCase__      :      str         =		torch.tensor([480, 640]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,     a   )   )
							# verify size
							lowerCAmelCase__      :      Union[str, Any]         =		torch.tensor([800, 1_066]   )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,     a   )   )

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__          =		get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  ReformerTokenizer
				lowercase			    =  ReformerTokenizerFast
				lowercase			    =  True
				lowercase			    =  False
				lowercase			    =  True






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							super().setUp()

							lowerCAmelCase__      :      int         =		ReformerTokenizer(a    ,     keep_accents=a   )
							tokenizer.save_pretrained(self.tmpdirname   )






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		'<s>'
							lowerCAmelCase__      :      Optional[int]         =		1

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






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		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(a   )    ,     1_000   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							self.assertEqual(self.get_tokenizer().vocab_size    ,     1_000   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							if not self.test_rust_tokenizer:
										return

							lowerCAmelCase__      :      Optional[int]         =		self.get_tokenizer()
							lowerCAmelCase__      :      Any         =		self.get_rust_tokenizer()

							lowerCAmelCase__      :      Optional[int]         =		'I was born in 92000, and this is falsé.'

							lowerCAmelCase__      :      List[Any]         =		tokenizer.tokenize(a   )
							lowerCAmelCase__      :      Optional[int]         =		rust_tokenizer.tokenize(a   )
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      List[str]         =		tokenizer.encode(a    ,     add_special_tokens=a   )
							lowerCAmelCase__      :      Any         =		rust_tokenizer.encode(a    ,     add_special_tokens=a   )
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      Any         =		self.get_rust_tokenizer()
							lowerCAmelCase__      :      List[str]         =		tokenizer.encode(a   )
							lowerCAmelCase__      :      Optional[int]         =		rust_tokenizer.encode(a   )
							self.assertListEqual(a    ,     a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Union[str, Any]=15   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      List[str]         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )

													# Simple input
													lowerCAmelCase__      :      Any         =		'This is a simple input'
													lowerCAmelCase__      :      str         =		['This is a simple input 1', 'This is a simple input 2']
													lowerCAmelCase__      :      Optional[int]         =		('This is a simple input', 'This is a pair')
													lowerCAmelCase__      :      Optional[int]         =		[
													    ('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(a    ,     tokenizer_r.encode    ,     a    ,     max_length=a    ,     padding='max_length'   )

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

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

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

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

													# Pair input
													self.assertRaises(
													    a    ,     tokenizer_r.batch_encode_plus    ,     a    ,     max_length=a    ,     padding='max_length'    ,     )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		ReformerTokenizer(a    ,     keep_accents=a   )

							lowerCAmelCase__      :      Dict         =		tokenizer.tokenize('This is a test'   )
							self.assertListEqual(a    ,     ['▁This', '▁is', '▁a', '▁t', 'est']   )

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

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

							lowerCAmelCase__      :      str         =		tokenizer.convert_ids_to_tokens(a   )
							self.assertListEqual(
							    a    ,     [
							        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 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'   )






				@slow
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		'Hello World!'
							lowerCAmelCase__      :      int         =		[126, 32, 262, 152, 38, 72, 287]

							self.assertListEqual(a    ,     self.big_tokenizer.encode(a   )   )






				@slow
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		(
							    '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'
							)
							lowerCAmelCase__      :      int         =		[
							    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(a    ,     self.big_tokenizer.encode(a   )   )






				@require_torch
				@slow
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							import torch

							from transformers import ReformerConfig, ReformerModel

							# Build sequence
							lowerCAmelCase__      :      Dict         =		list(self.big_tokenizer.get_vocab().keys()   )[:10]
							lowerCAmelCase__      :      Optional[Any]         =		' '.join(a   )
							lowerCAmelCase__      :      Any         =		self.big_tokenizer.encode_plus(a    ,     return_tensors='pt'   )
							lowerCAmelCase__      :      Optional[int]         =		self.big_tokenizer.batch_encode_plus([sequence, sequence]    ,     return_tensors='pt'   )

							lowerCAmelCase__      :      List[Any]         =		ReformerConfig()
							# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
							lowerCAmelCase__      :      int         =		encoded_sequence['input_ids'].shape
							lowerCAmelCase__      :      Union[str, Any]         =		ReformerModel(a   )

							# 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(**a   )
										model(**a   )






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		{'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
							lowerCAmelCase__      :      List[str]         =		[
							    'This is a very simple sentence.',
							    'The quick brown fox jumps over the lazy dog.',
							]

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