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 DeformableDetrImageProcessor





class 							lowercase						(						unittest.TestCase ):


   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	List[str]=7   ,		__lowerCAmelCase				:	Optional[Any]=3   ,		__lowerCAmelCase				:	List[str]=30   ,		__lowerCAmelCase				:	Optional[int]=400   ,		__lowerCAmelCase				:	str=True   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		__lowerCAmelCase				:	Dict=True   ,		__lowerCAmelCase				:	List[str]=[0.5, 0.5, 0.5]   ,		__lowerCAmelCase				:	Tuple=[0.5, 0.5, 0.5]   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	int=1 / 255   ,		__lowerCAmelCase				:	List[Any]=True   ,		)      ->					Any:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         lowercase_     =      size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      num_channels
         lowercase_     =      min_resolution
         lowercase_     =      max_resolution
         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      do_normalize
         lowercase_     =      image_mean
         lowercase_     =      image_std
         lowercase_     =      do_rescale
         lowercase_     =      rescale_factor
         lowercase_     =      do_pad


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[str]:
         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 __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Union[str, Any]=False)      ->					Union[str, Any]:
         if not batched:
               lowercase_     =      image_inputs[0]
               if isinstance(__lowerCAmelCase   ,		Image.Image):
                     lowercase_ ,							lowercase_     =      image.size
               else:
                     lowercase_ ,							lowercase_     =      image.shape[1], image.shape[2]
               if w < h:
                     lowercase_     =      int(self.size["shortest_edge"] * h / w)
                     lowercase_     =      self.size["shortest_edge"]
               elif w > h:
                     lowercase_     =      self.size["shortest_edge"]
                     lowercase_     =      int(self.size["shortest_edge"] * w / h)
               else:
                     lowercase_     =      self.size["shortest_edge"]
                     lowercase_     =      self.size["shortest_edge"]

         else:
               lowercase_     =      []
               for image in image_inputs:
                     lowercase_ ,							lowercase_     =      self.get_expected_values([image])
                     expected_values.append((expected_height, expected_width))
               lowercase_     =      max(__lowerCAmelCase   ,		key=lambda __lowerCAmelCase: item[0])[0]
               lowercase_     =      max(__lowerCAmelCase   ,		key=lambda __lowerCAmelCase: item[1])[1]

         return expected_height, expected_width





@require_torch
@require_vision
class 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=DeformableDetrImageProcessor if is_vision_available() else None


   def __UpperCAmelCase       (   self				:	Any)      ->					Tuple:
         lowercase_     =      DeformableDetrImageProcessingTester(self)


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[str]:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Union[str, Any]:
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"image_std"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_rescale"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_pad"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"size"))


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size   ,		{"shortest_edge": 18, "longest_edge": 1333})
         self.assertEqual(image_processor.do_pad   ,		__lowerCAmelCase)

         lowercase_     =      self.image_processing_class.from_dict(
             self.image_processor_dict   ,		size=42   ,		max_size=84   ,		pad_and_return_pixel_mask=__lowerCAmelCase)
         self.assertEqual(image_processor.size   ,		{"shortest_edge": 42, "longest_edge": 84})
         self.assertEqual(image_processor.do_pad   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int)      ->					Union[str, Any]:
         pass


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		Image.Image)

         # Test not batched input
         lowercase_     =      image_processing(image_inputs[0]   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase)

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

         # Test batched
         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

         lowercase_     =      image_processing(__lowerCAmelCase   ,		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 __UpperCAmelCase       (   self				:	List[str])      ->					Union[str, Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase   ,		numpify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		np.ndarray)

         # Test not batched input
         lowercase_     =      image_processing(image_inputs[0]   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase)

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

         # Test batched
         lowercase_     =      image_processing(__lowerCAmelCase   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

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


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Optional[Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase   ,		torchify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		torch.Tensor)

         # Test not batched input
         lowercase_     =      image_processing(image_inputs[0]   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase)

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

         # Test batched
         lowercase_     =      image_processing(__lowerCAmelCase   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

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


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Optional[Any]:
         # prepare image and target
         lowercase_     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,		"r") as f:
               lowercase_     =      json.loads(f.read())

         lowercase_     =      {"image_id": 3_9769, "annotations": target}

         # encode them
         lowercase_     =      DeformableDetrImageProcessor()
         lowercase_     =      image_processing(images=__lowerCAmelCase   ,		annotations=__lowerCAmelCase   ,		return_tensors="pt")

         # verify pixel values
         lowercase_     =      torch.Size([1, 3, 800, 1066])
         self.assertEqual(encoding["pixel_values"].shape   ,		__lowerCAmelCase)

         lowercase_     =      torch.tensor([0.2796, 0.3138, 0.3481])
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,		__lowerCAmelCase   ,		atol=1e-4))

         # verify area
         lowercase_     =      torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,		__lowerCAmelCase))
         # verify boxes
         lowercase_     =      torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape   ,		__lowerCAmelCase)
         lowercase_     =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,		__lowerCAmelCase   ,		atol=1e-3))
         # verify image_id
         lowercase_     =      torch.tensor([3_9769])
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,		__lowerCAmelCase))
         # verify is_crowd
         lowercase_     =      torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,		__lowerCAmelCase))
         # verify class_labels
         lowercase_     =      torch.tensor([75, 75, 63, 65, 17, 17])
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,		__lowerCAmelCase))
         # verify orig_size
         lowercase_     =      torch.tensor([480, 640])
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,		__lowerCAmelCase))
         # verify size
         lowercase_     =      torch.tensor([800, 1066])
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,		__lowerCAmelCase))


   @slow
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         # prepare image, target and masks_path
         lowercase_     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,		"r") as f:
               lowercase_     =      json.loads(f.read())

         lowercase_     =      {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

         lowercase_     =      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

         # encode them
         lowercase_     =      DeformableDetrImageProcessor(format="coco_panoptic")
         lowercase_     =      image_processing(images=__lowerCAmelCase   ,		annotations=__lowerCAmelCase   ,		masks_path=__lowerCAmelCase   ,		return_tensors="pt")

         # verify pixel values
         lowercase_     =      torch.Size([1, 3, 800, 1066])
         self.assertEqual(encoding["pixel_values"].shape   ,		__lowerCAmelCase)

         lowercase_     =      torch.tensor([0.2796, 0.3138, 0.3481])
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,		__lowerCAmelCase   ,		atol=1e-4))

         # verify area
         lowercase_     =      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,		__lowerCAmelCase))
         # verify boxes
         lowercase_     =      torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape   ,		__lowerCAmelCase)
         lowercase_     =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,		__lowerCAmelCase   ,		atol=1e-3))
         # verify image_id
         lowercase_     =      torch.tensor([3_9769])
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,		__lowerCAmelCase))
         # verify is_crowd
         lowercase_     =      torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,		__lowerCAmelCase))
         # verify class_labels
         lowercase_     =      torch.tensor([17, 17, 63, 75, 75, 93])
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,		__lowerCAmelCase))
         # verify masks
         lowercase_     =      82_2873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,		__lowerCAmelCase)
         # verify orig_size
         lowercase_     =      torch.tensor([480, 640])
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,		__lowerCAmelCase))
         # verify size
         lowercase_     =      torch.tensor([800, 1066])
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,		__lowerCAmelCase))







'''simple docstring'''

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[Any]						  =      get_tests_dir("fixtures/test_sentencepiece.model")





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


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[Any]:
         super().setUp()

         lowercase_     =      ReformerTokenizer(__lowerCAmelCase   ,		keep_accents=__lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def __UpperCAmelCase       (   self				:	int)      ->					int:
         lowercase_     =      "<s>"
         lowercase_     =      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				:	Tuple)      ->					Optional[int]:
         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]   ,		"j")
         self.assertEqual(len(__lowerCAmelCase)   ,		1000)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size   ,		1000)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					List[Any]:
         if not self.test_rust_tokenizer:
               return

         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_rust_tokenizer()

         lowercase_     =      "I was born in 92000, and this is falsé."

         lowercase_     =      tokenizer.tokenize(__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.tokenize(__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      tokenizer.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      self.get_rust_tokenizer()
         lowercase_     =      tokenizer.encode(__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.encode(__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[str]=15)      ->					Tuple:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                     lowercase_     =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase   ,		**__lowerCAmelCase)

                     # Simple input
                     lowercase_     =      "This is a simple input"
                     lowercase_     =      ["This is a simple input 1", "This is a simple input 2"]
                     lowercase_     =      ("This is a simple input", "This is a pair")
                     lowercase_     =      [
                         ("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)      ->					Tuple:
         pass


   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[Any]:
         lowercase_     =      ReformerTokenizer(__lowerCAmelCase   ,		keep_accents=__lowerCAmelCase)

         lowercase_     =      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]   ,		)

         lowercase_     =      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",
                 ".",
             ]   ,		)
         lowercase_     =      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]   ,		)

         lowercase_     =      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				:	List[str])      ->					Any:
         return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")


   @slow
   def __UpperCAmelCase       (   self				:	Tuple)      ->					str:
         lowercase_     =      "Hello World!"
         lowercase_     =      [126, 32, 262, 152, 38, 72, 287]

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


   @slow
   def __UpperCAmelCase       (   self				:	Any)      ->					Dict:
         lowercase_     =      (
             "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"
         )
         lowercase_     =      [
             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				:	Any)      ->					Tuple:
         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         lowercase_     =      list(self.big_tokenizer.get_vocab().keys())[:10]
         lowercase_     =      " ".join(__lowerCAmelCase)
         lowercase_     =      self.big_tokenizer.encode_plus(__lowerCAmelCase   ,		return_tensors="pt")
         lowercase_     =      self.big_tokenizer.batch_encode_plus([sequence, sequence]   ,		return_tensors="pt")

         lowercase_     =      ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         lowercase_     =      encoded_sequence["input_ids"].shape
         lowercase_     =      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				:	List[Any])      ->					Tuple:
         # fmt: off
         lowercase_     =      {"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
         lowercase_     =      [
             "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   ,		)






