
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        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=3     ,	)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			image_size
													__UpperCamelCase       =			patch_size
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_labels
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			type_sequence_label_size
													__UpperCamelCase       =			initializer_range

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



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

													__UpperCamelCase       =			None
													if self.use_labels:
																			__UpperCamelCase       =			ids_tensor([self.batch_size]     ,	self.type_sequence_label_size			)

													__UpperCamelCase       =			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     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			FlaxBeitModel(config=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length - 1, self.vocab_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			self.type_sequence_label_size
													__UpperCamelCase       =			FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)			)

													# test greyscale images
													__UpperCamelCase       =			1
													__UpperCamelCase       =			FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase							
													)  ,							(
													    __UpperCamelCase							
													)  ,							(
													    __UpperCamelCase							
													)  ,														
													)       =			config_and_inputs
													__UpperCamelCase       =			{'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_flax
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






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



							def     A__		(  self			)->   None:





													'''simple docstring'''






													__UpperCamelCase       =			FlaxBeitModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCamelCase       =			['''pixel_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__			):
																									__UpperCamelCase       =			self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)
																									__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)

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

																									with self.subTest('''JIT Enabled'''			):
																															__UpperCamelCase       =			model_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

																									with self.subTest('''JIT Disabled'''			):
																															with jax.disable_jit():
																																					__UpperCamelCase       =			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     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase       =			model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''			)
																			__UpperCamelCase       =			model(np.ones((1, 3, 224, 224)			)			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





def 	A_ (							)      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
						return image






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

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''			) if is_vision_available() else None



							@slow
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''			).pixel_values

													# prepare bool_masked_pos
													__UpperCamelCase       =			np.ones((1, 196)     ,	dtype=SCREAMING_SNAKE_CASE_			)

													# forward pass
													__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	bool_masked_pos=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			(1, 196, 8192)
													self.assertEqual(logits.shape     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			np.array(
													    [[-3.2_4_3_7, 0.5_0_7_2, -13.9174], [-3.2_4_5_6, 0.4_9_4_8, -13.9401], [-3.2_0_3_3, 0.5_1_2_1, -13.8550]]			)

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



							@slow
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''			)

													# forward pass
													__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			(1, 1000)
													self.assertEqual(logits.shape     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)

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

													__UpperCamelCase       =			281
													self.assertEqual(logits.argmax(-1			).item()     ,	SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''			)

													# forward pass
													__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			(1, 21841)
													self.assertEqual(logits.shape     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)

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

													__UpperCamelCase       =			2396
													self.assertEqual(logits.argmax(-1			).item()     ,	SCREAMING_SNAKE_CASE_			)






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




