



'''simple docstring'''





import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__      					=     get_tests_dir("fixtures/spiece.model")


@require_sentencepiece
@require_tokenizers
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :List[Any]						   =					DebertaVaTokenizer
       lowerCAmelCase__  :Tuple						   =					DebertaVaTokenizerFast
       lowerCAmelCase__  :Tuple						   =					True
       lowerCAmelCase__  :str						   =					True



       def 							_a      (				self      )  ->							Union[str, Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,unk_token="<unk>"      )
           tokenizer.save_pretrained(self.tmpdirname      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						"this is a test"
           lowercase__		       =						"this is a test"
           return input_text, output_text



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						"<pad>"
           lowercase__		       =						0

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



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						list(self.get_tokenizer().get_vocab().keys()      )
           self.assertEqual(vocab_keys[0]     ,"<pad>"      )
           self.assertEqual(vocab_keys[1]     ,"<unk>"      )
           self.assertEqual(vocab_keys[-1]     ,"[PAD]"      )
           self.assertEqual(len(UpperCAmelCase_      )     ,30_001      )



       def 							_a      (				self      )  ->							List[str]:
           self.assertEqual(self.get_tokenizer().vocab_size     ,30_000      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           # fmt: off
           lowercase__		       =						" \tHeLLo!how  \n Are yoU?  "
           lowercase__		       =						["▁hello", "!", "how", "▁are", "▁you", "?"]
           # fmt: on

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."      )
       def 							_a      (				self      )  ->							Optional[int]:
           pass



       @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."      )
       def 							_a      (				self      )  ->							Any:
           pass



       def 							_a      (				self      )  ->							Tuple:
           # fmt: off
           lowercase__		       =						"I was born in 92000, and this is falsé."
           lowercase__		       =						["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
           # fmt: on

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           # fmt: off
           lowercase__		       =						"I was born in 92000, and this is falsé."
           lowercase__		       =						["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
           # fmt: on

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           # fmt: off
           lowercase__		       =						"I was born in 92000, and this is falsé."
           lowercase__		       =						["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
           # fmt: on

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[int]:
           # fmt: off
           lowercase__		       =						"I was born in 92000, and this is falsé."
           lowercase__		       =						["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
           # fmt: on

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           # fmt: off
           lowercase__		       =						" \tHeLLo!how  \n Are yoU?  "
           lowercase__		       =						["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
           # fmt: on

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,do_lower_case=UpperCAmelCase_     ,split_by_punct=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )

           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.get_tokenizer()
           lowercase__		       =						self.get_rust_tokenizer()

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

           lowercase__		       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						self.get_rust_tokenizer()
           lowercase__		       =						tokenizer.encode(UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						"This is a test"
           lowercase__		       =						[13, 1, 4_398, 25, 21, 1_289]
           lowercase__		       =						["▁", "T", "his", "▁is", "▁a", "▁test"]
           lowercase__		       =						["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_     ,keep_accents=UpperCAmelCase_      )
           lowercase__		       =						DebertaVaTokenizerFast(UpperCAmelCase_     ,keep_accents=UpperCAmelCase_      )

           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						tokenizer.tokenize(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.tokenize(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # fmt: off
           lowercase__		       =						"I was born in 92000, and this is falsé."
           lowercase__		       =						[13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
           lowercase__		       =						["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
           lowercase__		       =						["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
           # fmt: on

           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						tokenizer.tokenize(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						tokenizer.convert_ids_to_tokens(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.tokenize(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						DebertaVaTokenizer(UpperCAmelCase_      )

           lowercase__		       =						tokenizer.encode("sequence builders"      )
           lowercase__		       =						tokenizer.encode("multi-sequence build"      )

           lowercase__		       =						tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_      )
           lowercase__		       =						tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_     ,UpperCAmelCase_      )

           self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]     ,UpperCAmelCase_      )
           self.assertEqual(
               [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]     ,UpperCAmelCase_     ,)







       @slow
       def 							_a      (				self      )  ->							str:
           # fmt: off
           lowercase__		       =						{"input_ids": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase_     ,model_name="microsoft/deberta-v2-xlarge"     ,revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"     ,)









'''simple docstring'''





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     BeitForImageClassification,
     BeitForMaskedImageModeling,
     BeitForSemanticSegmentation,
     BeitModel,
 )
 from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 import PIL
 from PIL import Image

 from transformers import BeitImageProcessor


class 				snake_case :



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=100     ,UpperCAmelCase_=13     ,UpperCAmelCase_=30     ,UpperCAmelCase_=2     ,UpperCAmelCase_=3     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_=32     ,UpperCAmelCase_=4     ,UpperCAmelCase_=4     ,UpperCAmelCase_=37     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=10     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=3     ,UpperCAmelCase_=None     ,UpperCAmelCase_=[0, 1, 2, 3]     ,)  ->							Any:
           lowercase__		       =						parent
           lowercase__		       =						100
           lowercase__		       =						batch_size
           lowercase__		       =						image_size
           lowercase__		       =						patch_size
           lowercase__		       =						num_channels
           lowercase__		       =						is_training
           lowercase__		       =						use_labels
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						type_sequence_label_size
           lowercase__		       =						initializer_range
           lowercase__		       =						scope
           lowercase__		       =						out_indices
           lowercase__		       =						num_labels

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowercase__		       =						(image_size // patch_size) ** 2
           lowercase__		       =						num_patches + 1



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           lowercase__		       =						None
           lowercase__		       =						None
           if self.use_labels:
               lowercase__		       =						ids_tensor([self.batch_size]     ,self.type_sequence_label_size      )
               lowercase__		       =						ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels      )

           lowercase__		       =						self.get_config()

           return config, pixel_values, labels, pixel_labels



       def 							_a      (				self      )  ->							List[str]:
           return BeitConfig(
               vocab_size=self.vocab_size     ,image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=UpperCAmelCase_     ,initializer_range=self.initializer_range     ,out_indices=self.out_indices     ,)



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						BeitModel(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						BeitForMaskedImageModeling(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						BeitForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						BeitForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						self.num_labels
           lowercase__		       =						BeitForSemanticSegmentation(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(
               result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )







       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.prepare_config_and_inputs()
           lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_torch
class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[Any]						   =					(
           (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__  :Optional[int]						   =					(
           {
               "feature-extraction": BeitModel,
               "image-classification": BeitForImageClassification,
               "image-segmentation": BeitForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__  :Any						   =					False
       lowerCAmelCase__  :Optional[Any]						   =					False
       lowerCAmelCase__  :Optional[Any]						   =					False



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,has_text_modality=UpperCAmelCase_     ,hidden_size=37      )



       def 							_a      (				self      )  ->							Tuple:
           self.config_tester.run_common_tests()



       @unittest.skip(reason="BEiT does not use inputs_embeds"      )
       def 							_a      (				self      )  ->							Dict:
           pass



       @require_torch_multi_gpu
       @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"      )
       def 							_a      (				self      )  ->							int:
           pass



       def 							_a      (				self      )  ->							Any:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)      )
               lowercase__		       =						model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(UpperCAmelCase_     ,nn.Linear      )      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               lowercase__		       =						inspect.signature(model.forward      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__		       =						[*signature.parameters.keys()]

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           if not self.model_tester.is_training:
               return

           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__		       =						True

           for model_class in self.all_model_classes:
               # we don't test BeitForMaskedImageModeling
               if model_class in [*get_values(UpperCAmelCase_      ), BeitForMaskedImageModeling]:
                   continue

               lowercase__		       =						model_class(UpperCAmelCase_      )
               model.to(UpperCAmelCase_      )
               model.train()
               lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_     ,return_labels=UpperCAmelCase_      )
               lowercase__		       =						model(**UpperCAmelCase_      ).loss
               loss.backward()



       def 							_a      (				self      )  ->							str:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
               return

           lowercase__		       =						False
           lowercase__		       =						True

           for model_class in self.all_model_classes:
               # we don't test BeitForMaskedImageModeling
               if (
                   model_class in [*get_values(UpperCAmelCase_      ), BeitForMaskedImageModeling]
                   or not model_class.supports_gradient_checkpointing
               ):
                   continue

               lowercase__		       =						model_class(UpperCAmelCase_      )
               model.gradient_checkpointing_enable()
               model.to(UpperCAmelCase_      )
               model.train()
               lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_     ,return_labels=UpperCAmelCase_      )
               lowercase__		       =						model(**UpperCAmelCase_      ).loss
               loss.backward()



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__		       =						_config_zero_init(UpperCAmelCase_      )
           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(config=UpperCAmelCase_      )
               for name, param in model.named_parameters():
                   # we skip lambda parameters as these require special initial values
                   # determined by config.layer_scale_init_value
                   if "lambda" in name:
                       continue
                   if param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()     ,[0.0, 1.0]     ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,)







       @slow
       def 							_a      (				self      )  ->							Optional[Any]:
           for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__		       =						BeitModel.from_pretrained(UpperCAmelCase_      )
               self.assertIsNotNone(UpperCAmelCase_      )





def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image



@require_torch
@require_vision
class 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Any:
           return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None



       @slow
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      ).to(UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values.to(UpperCAmelCase_      )

           # prepare bool_masked_pos
           lowercase__		       =						torch.ones((1, 196)     ,dtype=torch.bool      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(pixel_values=UpperCAmelCase_     ,bool_masked_pos=UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 196, 8_192)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor(
               [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,UpperCAmelCase_     ,atol=1E-2      )      )



       @slow
       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 1_000)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([-1.23_85, -1.09_87, -1.01_08]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						281
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
               UpperCAmelCase_      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 21_841)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([1.68_81, -0.27_87, 0.59_01]      ).to(UpperCAmelCase_      )

           self.assertTrue(torch.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						2_396
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

           lowercase__		       =						BeitImageProcessor(do_resize=UpperCAmelCase_     ,size=640     ,do_center_crop=UpperCAmelCase_      )

           lowercase__		       =						load_dataset("hf-internal-testing/fixtures_ade20k"     ,split="test"      )
           lowercase__		       =						Image.open(ds[0]["file"]      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						torch.Size((1, 150, 160, 160)      )
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						version.parse(PIL.__version__      ) < version.parse("9.0.0"      )

           if is_pillow_less_than_a:
               lowercase__		       =						torch.tensor(
                   [
                       [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                       [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                       [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                   ]     ,device=UpperCAmelCase_     ,)
           else:
               lowercase__		       =						torch.tensor(
                   [
                       [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                       [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                       [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                   ]     ,device=UpperCAmelCase_     ,)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )







       @slow
       def 							_a      (				self      )  ->							str:
           lowercase__		       =						BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
           lowercase__		       =						model.to(UpperCAmelCase_      )

           lowercase__		       =						BeitImageProcessor(do_resize=UpperCAmelCase_     ,size=640     ,do_center_crop=UpperCAmelCase_      )

           lowercase__		       =						load_dataset("hf-internal-testing/fixtures_ade20k"     ,split="test"      )
           lowercase__		       =						Image.open(ds[0]["file"]      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )

           lowercase__		       =						outputs.logits.detach().cpu()

           lowercase__		       =						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_     ,target_sizes=[(500, 300)]      )
           lowercase__		       =						torch.Size((500, 300)      )
           self.assertEqual(segmentation[0].shape     ,UpperCAmelCase_      )

           lowercase__		       =						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_      )
           lowercase__		       =						torch.Size((160, 160)      )
           self.assertEqual(segmentation[0].shape     ,UpperCAmelCase_      )




