




"""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       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def __init__(     self					:	Tuple    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	str=1_0_0    ,   lowercase__					:	Union[str, Any]=1_3    ,   lowercase__					:	List[Any]=3_0    ,   lowercase__					:	int=2    ,   lowercase__					:	Union[str, Any]=3    ,   lowercase__					:	int=True    ,   lowercase__					:	int=True    ,   lowercase__					:	List[str]=3_2    ,   lowercase__					:	Optional[Any]=5    ,   lowercase__					:	int=4    ,   lowercase__					:	Any=3_7    ,   lowercase__					:	int="gelu"    ,   lowercase__					:	int=0.1    ,   lowercase__					:	Optional[int]=0.1    ,   lowercase__					:	str=1_0    ,   lowercase__					:	List[Any]=0.0_2    ,   lowercase__					:	Dict=3    ,   ):
						__lowercase				:		Any												=				parent
						__lowercase				:		Optional[Any]												=				vocab_size
						__lowercase				:		Any												=				batch_size
						__lowercase				:		List[str]												=				image_size
						__lowercase				:		Tuple												=				patch_size
						__lowercase				:		Dict												=				num_channels
						__lowercase				:		List[Any]												=				is_training
						__lowercase				:		Dict												=				use_labels
						__lowercase				:		List[str]												=				hidden_size
						__lowercase				:		Dict												=				num_hidden_layers
						__lowercase				:		Optional[Any]												=				num_attention_heads
						__lowercase				:		Dict												=				intermediate_size
						__lowercase				:		List[Any]												=				hidden_act
						__lowercase				:		List[str]												=				hidden_dropout_prob
						__lowercase				:		Optional[Any]												=				attention_probs_dropout_prob
						__lowercase				:		Any												=				type_sequence_label_size
						__lowercase				:		Any												=				initializer_range

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



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Union[str, Any]												=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						__lowercase				:		int												=				None
						if self.use_labels:
								__lowercase				:		str												=				ids_tensor([self.batch_size]    ,   self.type_sequence_label_size						)

						__lowercase				:		List[Any]												=				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=lowercase__    ,   initializer_range=self.initializer_range    ,   )

						return config, pixel_values, labels



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



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



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

						# test greyscale images
						__lowercase				:		List[Any]												=				1
						__lowercase				:		Union[str, Any]												=				FlaxBeitForImageClassification(lowercase__						)

						__lowercase				:		Tuple												=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
						__lowercase				:		Any												=				model(lowercase__						)



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Union[str, Any]												=				self.prepare_config_and_inputs()
						(
						    (
						    __lowercase					
						)						,(
						    __lowercase					
						)						,(
						    __lowercase					
						)						,					
						)				:		List[Any]												=				config_and_inputs
						__lowercase				:		Dict												=				{"pixel_values": pixel_values}
						return config, inputs_dict





@require_flax
class       lowerCAmelCase__       (			lowercase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Tuple       =      (
				    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
				)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Tuple												=				FlaxBeitModelTester(self						)
						__lowercase				:		Optional[Any]												=				ConfigTester(self    ,   config_class=lowercase__    ,   has_text_modality=lowercase__    ,   hidden_size=3_7						)



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



				def       snake_case    (     self					:	str						):
						__lowercase						,__lowercase				:		Union[str, Any]												=				self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase				:		Optional[int]												=				["pixel_values"]
								self.assertListEqual(arg_names[:1]    ,   lowercase__						)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase						,__lowercase				:		Dict												=				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__						):
										__lowercase				:		Dict												=				self._prepare_for_class(lowercase__    ,   lowercase__						)
										__lowercase				:		Dict												=				model_class(lowercase__						)

										@jax.jit
										def model_jitted(lowercase__					:	Union[str, Any]    ,   **lowercase__					:	Tuple						):
												return model(pixel_values=lowercase__    ,   **lowercase__						)

										with self.subTest("JIT Enabled"						):
												__lowercase				:		str												=				model_jitted(**lowercase__						).to_tuple()

										with self.subTest("JIT Disabled"						):
												with jax.disable_jit():
														__lowercase				:		int												=				model_jitted(**lowercase__						).to_tuple()

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



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



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



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



				@slow
				def       snake_case    (     self					:	Optional[Any]						):
						for model_class_name in self.all_model_classes:
								__lowercase				:		Union[str, Any]												=				model_class_name.from_pretrained("microsoft/beit-base-patch16-224"						)
								__lowercase				:		Dict												=				model(np.ones((1, 3, 2_2_4, 2_2_4)						)						)
								self.assertIsNotNone(lowercase__						)





def 						snake_case__       (					)			->str:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image





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


				"""simple docstring"""





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



				@slow
				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		List[str]												=				FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"						)

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

						# prepare bool_masked_pos
						__lowercase				:		Optional[int]												=				np.ones((1, 1_9_6)    ,   dtype=lowercase__						)

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

						# verify the logits
						__lowercase				:		Optional[int]												=				(1, 1_9_6, 8_1_9_2)
						self.assertEqual(logits.shape    ,   lowercase__						)

						__lowercase				:		Optional[int]												=				np.array(
						    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]						)

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



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

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

						# forward pass
						__lowercase				:		Optional[Any]												=				model(**lowercase__						)
						__lowercase				:		List[str]												=				outputs.logits

						# verify the logits
						__lowercase				:		Optional[int]												=				(1, 1_0_0_0)
						self.assertEqual(logits.shape    ,   lowercase__						)

						__lowercase				:		List[str]												=				np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]						)

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

						__lowercase				:		Optional[int]												=				2_8_1
						self.assertEqual(logits.argmax(-1						).item()    ,   lowercase__						)



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

						__lowercase				:		Dict												=				self.default_image_processor
						__lowercase				:		Optional[int]												=				prepare_img()
						__lowercase				:		List[str]												=				image_processor(images=lowercase__    ,   return_tensors="np"						)

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

						# verify the logits
						__lowercase				:		List[Any]												=				(1, 2_1_8_4_1)
						self.assertEqual(logits.shape    ,   lowercase__						)

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

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

						__lowercase				:		Optional[int]												=				2_3_9_6
						self.assertEqual(logits.argmax(-1						).item()    ,   lowercase__						)






"""simple docstring"""







import argparse
from collections import defaultdict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				F'{file}_{class_name}_{test_name}'
		done_test[_id] += 1

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		Union[str, Any]												=				f.readlines()

		__lowercase				:		str												=				F'class {class_name}('
		__lowercase				:		Union[str, Any]												=				F'{4 * " "}def {test_name}('
		__lowercase				:		List[Any]												=				F'{8 * " "}{correct_line.split()[0]}'
		__lowercase				:		Optional[int]												=				F'{16 * " "}{correct_line.split()[0]}'
		__lowercase				:		List[Any]												=				False
		__lowercase				:		int												=				False
		__lowercase				:		int												=				False
		__lowercase				:		List[Any]												=				False
		__lowercase				:		str												=				0
		__lowercase				:		Dict												=				0

		__lowercase				:		Optional[int]												=				[]
		for line in lines:
				if line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and in_func and (line.startswith(_lowerCamelCase				) or line.startswith(_lowerCamelCase				)):
						__lowercase				:		int												=				len(line.split(correct_line.split()[0]				)[0]				)
						count += 1

						if count == done_test[_id]:
								__lowercase				:		Any												=				True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								__lowercase				:		int												=				True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'{spaces * " "}{correct_line}'				)
						__lowercase				:		List[Any]												=				False
				else:
						new_lines.append(_lowerCamelCase				)

		with open(_lowerCamelCase,  "w"				) as f:
				for line in new_lines:
						f.write(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->List[Any]:






		"""simple docstring"""


		if fail is not None:
				with open(_lowerCamelCase,  "r"				) as f:
						__lowercase				:		str												=				{l.strip() for l in f.readlines()}
		else:
				__lowercase				:		List[Any]												=				None

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		str												=				f.readlines()

		__lowercase				:		List[str]												=				defaultdict(_lowerCamelCase				)
		for line in correct_lines:
				__lowercase						,__lowercase						,__lowercase						,__lowercase				:		str												=				line.split(";"				)
				if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
						overwrite_file(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)


if __name__ == "__main__":
						__A   :  str					=      argparse.ArgumentParser()
						parser.add_argument('--correct_filename', help='filename of tests with expected result')
						parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
						__A   :  Union[str, Any]					=      parser.parse_args()

						main(args.correct_filename, args.fail_filename)
