



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
				from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

lowerCAmelCase__					  =  {'target_lang': 'fi', 'source_lang': 'en'}
lowerCAmelCase__					  =  '>>zh<<'
lowerCAmelCase__					  =  'Helsinki-NLP/'

if is_torch_available():
				lowerCAmelCase__					  =  'pt'
elif is_tf_available():
				lowerCAmelCase__					  =  'tf'
else:
				lowerCAmelCase__					  =  'jax'

@require_sentencepiece
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		MarianTokenizer
					lowercase_								=		False
					lowercase_								=		True







					def 			snake_case					(							self :     Tuple					):
									super().setUp()
									lowercase__					:   Optional[int]													=					['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
									lowercase__					:   Tuple													=					dict(zip(SCREAMING_SNAKE_CASE				,       range(len(SCREAMING_SNAKE_CASE					)					)					)					)
									lowercase__					:   int													=					Path(self.tmpdirname					)
									save_json(SCREAMING_SNAKE_CASE				,       save_dir / VOCAB_FILES_NAMES["vocab"]					)
									save_json(SCREAMING_SNAKE_CASE				,       save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]					)
									if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
													copyfile(SCREAMING_SNAKE_CASE				,       save_dir / VOCAB_FILES_NAMES["source_spm"]					)
													copyfile(SCREAMING_SNAKE_CASE				,       save_dir / VOCAB_FILES_NAMES["target_spm"]					)

									lowercase__					:   List[str]													=					MarianTokenizer.from_pretrained(self.tmpdirname					)
									tokenizer.save_pretrained(self.tmpdirname					)







					def 			snake_case					(							self :     Any				,       **SCREAMING_SNAKE_CASE :     Any					):
									return MarianTokenizer.from_pretrained(self.tmpdirname				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									return (
									    "This is a test",
									    "This is a test",
									)







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Dict													=					'''</s>'''
									lowercase__					:   Union[str, Any]													=					0

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







					def 			snake_case					(							self :     str					):
									lowercase__					:   List[Any]													=					list(self.get_tokenizer().get_vocab().keys()					)

									self.assertEqual(vocab_keys[0]				,       "</s>"					)
									self.assertEqual(vocab_keys[1]				,       "<unk>"					)
									self.assertEqual(vocab_keys[-1]				,       "<pad>"					)
									self.assertEqual(len(SCREAMING_SNAKE_CASE					)				,       9					)







					def 			snake_case					(							self :     Optional[Any]					):
									self.assertEqual(self.get_tokenizer().vocab_size				,       9					)







					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   List[Any]													=					MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""					)
									lowercase__					:   str													=					en_de_tokenizer(["I am a small frog"]				,       return_tensors=SCREAMING_SNAKE_CASE					)
									self.assertIsInstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					[38, 121, 14, 697, 38_848, 0]
									self.assertListEqual(SCREAMING_SNAKE_CASE				,       batch.input_ids[0]					)

									lowercase__					:   Optional[int]													=					tempfile.mkdtemp()
									en_de_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[str]													=					[x.name for x in Path(SCREAMING_SNAKE_CASE					).glob("*"					)]
									self.assertIn("source.spm"				,       SCREAMING_SNAKE_CASE					)
									MarianTokenizer.from_pretrained(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Any													=					self.get_tokenizer()

									lowercase__					:   List[Any]													=					tok(
									    ["I am a small frog" * 1_000, "I am a small frog"]				,       padding=SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE					)
									self.assertIsInstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									self.assertEqual(batch.input_ids.shape				,       (2, 512)					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   int													=					self.get_tokenizer()
									lowercase__					:   Union[str, Any]													=					tok(["I am a tiny frog", "I am a small frog"]				,       padding=SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE					)
									self.assertIsInstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									self.assertEqual(batch_smaller.input_ids.shape				,       (2, 10)					)







					@slow
					def 			snake_case					(							self :     Dict					):
									# fmt: off
									lowercase__					:   str													=					{'''input_ids''': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '''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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=SCREAMING_SNAKE_CASE				,       model_name="Helsinki-NLP/opus-mt-en-de"				,       revision="1a8c2263da11e68e50938f97e10cd57820bd504c"				,       decode_kwargs={"use_source_tokenizer": True}				,       )







					def 			snake_case					(							self :     str					):
									lowercase__					:   Optional[int]													=					MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"					)

									lowercase__					:   Optional[Any]													=					'''Tämä on testi'''
									lowercase__					:   int													=					'''This is a test'''

									lowercase__					:   Dict													=					[76, 7, 2_047, 2]
									lowercase__					:   Optional[Any]													=					[69, 12, 11, 940, 2]

									lowercase__					:   Optional[int]													=					tokenizer(SCREAMING_SNAKE_CASE					).input_ids
									self.assertListEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					tokenizer(text_target=SCREAMING_SNAKE_CASE					).input_ids
									self.assertListEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					tokenizer.decode(SCREAMING_SNAKE_CASE				,       skip_special_tokens=SCREAMING_SNAKE_CASE					)
									self.assertEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)









import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
				import jax

				from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]=100				,       SCREAMING_SNAKE_CASE :     Tuple=13				,       SCREAMING_SNAKE_CASE :     str=30				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=2				,       SCREAMING_SNAKE_CASE :     Tuple=3				,       SCREAMING_SNAKE_CASE :     List[Any]=True				,       SCREAMING_SNAKE_CASE :     int=True				,       SCREAMING_SNAKE_CASE :     Dict=32				,       SCREAMING_SNAKE_CASE :     str=5				,       SCREAMING_SNAKE_CASE :     Any=4				,       SCREAMING_SNAKE_CASE :     str=37				,       SCREAMING_SNAKE_CASE :     Any="gelu"				,       SCREAMING_SNAKE_CASE :     Tuple=0.1				,       SCREAMING_SNAKE_CASE :     Optional[Any]=0.1				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=10				,       SCREAMING_SNAKE_CASE :     Dict=0.02				,       SCREAMING_SNAKE_CASE :     Any=3				,       ):
									lowercase__					:   Optional[int]													=					parent
									lowercase__					:   Optional[int]													=					vocab_size
									lowercase__					:   Dict													=					batch_size
									lowercase__					:   List[Any]													=					image_size
									lowercase__					:   List[Any]													=					patch_size
									lowercase__					:   Tuple													=					num_channels
									lowercase__					:   Any													=					is_training
									lowercase__					:   str													=					use_labels
									lowercase__					:   List[Any]													=					hidden_size
									lowercase__					:   Optional[int]													=					num_hidden_layers
									lowercase__					:   Dict													=					num_attention_heads
									lowercase__					:   Optional[int]													=					intermediate_size
									lowercase__					:   int													=					hidden_act
									lowercase__					:   str													=					hidden_dropout_prob
									lowercase__					:   Union[str, Any]													=					attention_probs_dropout_prob
									lowercase__					:   int													=					type_sequence_label_size
									lowercase__					:   Optional[int]													=					initializer_range

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







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   List[Any]													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									lowercase__					:   Union[str, Any]													=					None
									if self.use_labels:
													lowercase__					:   List[Any]													=					ids_tensor([self.batch_size]				,       self.type_sequence_label_size					)

									lowercase__					:   int													=					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=SCREAMING_SNAKE_CASE				,       initializer_range=self.initializer_range				,       )

									return config, pixel_values, labels







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   Optional[Any]													=					FlaxBeitModel(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     List[str]					):
									lowercase__					:   int													=					FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length - 1, self.vocab_size)					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Any					):
									lowercase__					:   Tuple													=					self.type_sequence_label_size
									lowercase__					:   Optional[int]													=					FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)					)

									# test greyscale images
									lowercase__					:   int													=					1
									lowercase__					:   List[str]													=					FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
									lowercase__					:   List[str]													=					model(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   str													=					config_and_inputs
									lowercase__					:   Dict													=					{"pixel_values": pixel_values}
									return config, inputs_dict






@require_flax
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		(
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   List[Any]													=					FlaxBeitModelTester(self					)
									lowercase__					:   str													=					ConfigTester(self				,       config_class=SCREAMING_SNAKE_CASE				,       has_text_modality=SCREAMING_SNAKE_CASE				,       hidden_size=37					)







					def 			snake_case					(							self :     int					):
									self.config_tester.run_common_tests()







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   Tuple													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)
													lowercase__					:   str													=					inspect.signature(model.__call__					)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase__					:   Optional[int]													=					[*signature.parameters.keys()]

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







					def 			snake_case					(							self :     List[Any]					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   Optional[int]													=					self._prepare_for_class(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)

																	@jax.jit
																	def model_jitted(SCREAMING_SNAKE_CASE :     Optional[Any]				,       **SCREAMING_SNAKE_CASE :     List[Any]					):
																					return model(pixel_values=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

																	with self.subTest("JIT Enabled"					):
																					lowercase__					:   Union[str, Any]													=					model_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	with self.subTest("JIT Disabled"					):
																					with jax.disable_jit():
																									lowercase__					:   Optional[int]													=					model_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	self.assertEqual(len(SCREAMING_SNAKE_CASE					)				,       len(SCREAMING_SNAKE_CASE					)					)
																	for jitted_output, output in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																					self.assertEqual(jitted_output.shape				,       output.shape					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Union[str, Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     int					):
									lowercase__					:   List[Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     Optional[int]					):
									for model_class_name in self.all_model_classes:
													lowercase__					:   Any													=					model_class_name.from_pretrained("microsoft/beit-base-patch16-224"					)
													lowercase__					:   Optional[int]													=					model(np.ones((1, 3, 224, 224)					)					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)






def  __lowerCamelCase (       ):

				"""simple docstring"""


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

@require_vision
@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






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







					@slow
					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Tuple													=					FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"					)

									lowercase__					:   int													=					self.default_image_processor
									lowercase__					:   Union[str, Any]													=					prepare_img()
									lowercase__					:   str													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					).pixel_values

									# prepare bool_masked_pos
									lowercase__					:   Optional[Any]													=					np.ones((1, 196)				,       dtype=SCREAMING_SNAKE_CASE					)

									# forward pass
									lowercase__					:   Any													=					model(pixel_values=SCREAMING_SNAKE_CASE				,       bool_masked_pos=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Tuple													=					outputs.logits

									# verify the logits
									lowercase__					:   List[str]													=					(1, 196, 8_192)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					np.array(
									    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]					)

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







					@slow
					def 			snake_case					(							self :     Any					):
									lowercase__					:   Union[str, Any]													=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"					)

									lowercase__					:   Tuple													=					self.default_image_processor
									lowercase__					:   List[Any]													=					prepare_img()
									lowercase__					:   Optional[Any]													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)

									# forward pass
									lowercase__					:   str													=					model(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					outputs.logits

									# verify the logits
									lowercase__					:   List[str]													=					(1, 1_000)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Tuple													=					np.array([-1.2_385, -1.0_987, -1.0_108]					)

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

									lowercase__					:   str													=					281
									self.assertEqual(logits.argmax(-1					).item()				,       SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     str					):
									lowercase__					:   List[Any]													=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"					)

									lowercase__					:   Dict													=					self.default_image_processor
									lowercase__					:   Dict													=					prepare_img()
									lowercase__					:   List[Any]													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)

									# forward pass
									lowercase__					:   Union[str, Any]													=					model(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					outputs.logits

									# verify the logits
									lowercase__					:   int													=					(1, 21_841)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   str													=					np.array([1.6_881, -0.2_787, 0.5_901]					)

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

									lowercase__					:   Union[str, Any]													=					2_396
									self.assertEqual(logits.argmax(-1					).item()				,       SCREAMING_SNAKE_CASE					)




