




"""simple docstring"""
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 __lowercase(      unittest.TestCase				):




					'''simple docstring'''


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

								# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__lowerCamelCase					:	List[str]	       =				(image_size // patch_size) ** 2
								__lowerCamelCase					:	Union[str, Any]	       =				num_patches + 1
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

								__lowerCamelCase					:	Tuple	       =				None
								if self.use_labels:
											__lowerCamelCase					:	Optional[int]	       =				ids_tensor([self.batch_size]						,	self.type_sequence_label_size     )

								__lowerCamelCase					:	str	       =				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=__a						,	initializer_range=self.initializer_range						,	)

								return config, pixel_values, labels
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Any	       =				FlaxBeitModel(config=__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				model(__a     )
								self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				FlaxBeitForMaskedImageModeling(config=__a     )
								__lowerCamelCase					:	str	       =				model(__a     )
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length - 1, self.vocab_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	str	       =				self.type_sequence_label_size
								__lowerCamelCase					:	int	       =				FlaxBeitForImageClassification(config=__a     )
								__lowerCamelCase					:	int	       =				model(__a     )
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)     )

								# test greyscale images
								__lowerCamelCase					:	Dict	       =				1
								__lowerCamelCase					:	List[Any]	       =				FlaxBeitForImageClassification(__a     )

								__lowerCamelCase					:	Any	       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
								__lowerCamelCase					:	List[str]	       =				model(__a     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.prepare_config_and_inputs()
								(
								    (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      
								)					:	Optional[int]	       =				config_and_inputs
								__lowerCamelCase					:	Union[str, Any]	       =				{'pixel_values': pixel_values}
								return config, inputs_dict



@require_flax
class __lowercase(      lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						str     		= (
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				FlaxBeitModelTester(self     )
								__lowerCamelCase					:	str	       =				ConfigTester(self						,	config_class=__a						,	has_text_modality=__a						,	hidden_size=37     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCamelCase					:	Tuple	       =				['pixel_values']
											self.assertListEqual(arg_names[:1]						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__     ):
														__lowerCamelCase					:	List[str]	       =				self._prepare_for_class(__a						,	__a     )
														__lowerCamelCase					:	List[Any]	       =				model_class(__a     )

														@jax.jit
														def model_jitted(__a						,	**__a     ):
																	return model(pixel_values=__a						,	**__a     )

														with self.subTest('JIT Enabled'     ):
																	__lowerCamelCase					:	Union[str, Any]	       =				model_jitted(**__a     ).to_tuple()

														with self.subTest('JIT Disabled'     ):
																	with jax.disable_jit():
																				__lowerCamelCase					:	str	       =				model_jitted(**__a     ).to_tuple()

														self.assertEqual(len(__a     )						,	len(__a     )     )
														for jitted_output, output in zip(__a						,	__a     ):
																	self.assertEqual(jitted_output.shape						,	output.shape     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a     )





					@slow
					def     snake_case_				(    self     ):
								for model_class_name in self.all_model_classes:
											__lowerCamelCase					:	List[str]	       =				model_class_name.from_pretrained('microsoft/beit-base-patch16-224'     )
											__lowerCamelCase					:	str	       =				model(np.ones((1, 3, 224, 224)     )     )
											self.assertIsNotNone(__a     )





def 						UpperCAmelCase				(			)  ->     Any:
			__lowerCamelCase					:	List[Any]	       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			return image






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




					'''simple docstring'''


					@cached_property
					def     snake_case_				(    self     ):
								return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'     ) if is_vision_available() else None
					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'     )

								__lowerCamelCase					:	int	       =				self.default_image_processor
								__lowerCamelCase					:	Tuple	       =				prepare_img()
								__lowerCamelCase					:	str	       =				image_processor(images=__a						,	return_tensors='np'     ).pixel_values

								# prepare bool_masked_pos
								__lowerCamelCase					:	int	       =				np.ones((1, 196)						,	dtype=__a     )

								# forward pass
								__lowerCamelCase					:	Any	       =				model(pixel_values=__a						,	bool_masked_pos=__a     )
								__lowerCamelCase					:	List[Any]	       =				outputs.logits

								# verify the logits
								__lowerCamelCase					:	List[Any]	       =				(1, 196, 8192)
								self.assertEqual(logits.shape						,	__a     )

								__lowerCamelCase					:	str	       =				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]						,	__a						,	atol=1E-2     )     )
					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'     )

								__lowerCamelCase					:	Union[str, Any]	       =				self.default_image_processor
								__lowerCamelCase					:	Dict	       =				prepare_img()
								__lowerCamelCase					:	Optional[int]	       =				image_processor(images=__a						,	return_tensors='np'     )

								# forward pass
								__lowerCamelCase					:	Dict	       =				model(**__a     )
								__lowerCamelCase					:	int	       =				outputs.logits

								# verify the logits
								__lowerCamelCase					:	Optional[int]	       =				(1, 1000)
								self.assertEqual(logits.shape						,	__a     )

								__lowerCamelCase					:	List[str]	       =				np.array([-1.2_385, -1.0_987, -1.0_108]     )

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

								__lowerCamelCase					:	Tuple	       =				281
								self.assertEqual(logits.argmax(-1     ).item()						,	__a     )





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'     )

								__lowerCamelCase					:	Union[str, Any]	       =				self.default_image_processor
								__lowerCamelCase					:	Optional[int]	       =				prepare_img()
								__lowerCamelCase					:	Optional[int]	       =				image_processor(images=__a						,	return_tensors='np'     )

								# forward pass
								__lowerCamelCase					:	str	       =				model(**__a     )
								__lowerCamelCase					:	List[str]	       =				outputs.logits

								# verify the logits
								__lowerCamelCase					:	Union[str, Any]	       =				(1, 21841)
								self.assertEqual(logits.shape						,	__a     )

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

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

								__lowerCamelCase					:	Tuple	       =				2396
								self.assertEqual(logits.argmax(-1     ).item()						,	__a     )









"""simple docstring"""
import random
from typing import Any





def 						UpperCAmelCase				(			A__:    list     )  ->     list[Any]:
			for _ in range(len(A__     )     ):
						__lowerCamelCase					:	List[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase					:	Optional[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				data[b], data[a]
			return data


if __name__ == "__main__":
					a_				:    Any										=     [0, 1, 2, 3, 4, 5, 6, 7]
					a_				:    int										=     ['''python''', '''says''', '''hello''', '''!''']
					print('''Fisher-Yates Shuffle:''')
					print('''List''', integers, strings)
					print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



