
'''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   a_ (      unittest.TestCase					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase=1_00					,   UpperCAmelCase=13					,   UpperCAmelCase=30					,   UpperCAmelCase=2					,   UpperCAmelCase=3					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase=32					,   UpperCAmelCase=5					,   UpperCAmelCase=4					,   UpperCAmelCase=37					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=0.1					,   UpperCAmelCase=0.1					,   UpperCAmelCase=10					,   UpperCAmelCase=0.02					,   UpperCAmelCase=3					,   ):
				a_      				=						parent
				a_      				=						vocab_size
				a_      				=						batch_size
				a_      				=						image_size
				a_      				=						patch_size
				a_      				=						num_channels
				a_      				=						is_training
				a_      				=						use_labels
				a_      				=						hidden_size
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_size
				a_      				=						hidden_act
				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						type_sequence_label_size
				a_      				=						initializer_range

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

				a_      				=						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					,   )

				return config, pixel_values, labels




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						FlaxBeitModel(config=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase	)
				self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length - 1, self.vocab_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						self.type_sequence_label_size
				a_      				=						FlaxBeitForImageClassification(config=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)	)

				# test greyscale images
				a_      				=						1
				a_      				=						FlaxBeitForImageClassification(UpperCAmelCase	)

				a_      				=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				a_      				=						model(UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()
				(
				    (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  
				)      				=						config_and_inputs
				a_      				=						{"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_flax
class   a_ (      UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			int       =    (
			    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
			)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxBeitModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   has_text_modality=UpperCAmelCase					,   hidden_size=37	)




			def 					lowerCAmelCase__					(       self	):
				self.config_tester.run_common_tests()




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()

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

					a_      				=						["""pixel_values"""]
					self.assertListEqual(arg_names[:1]					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
						a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)
						a_      				=						model_class(UpperCAmelCase	)

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

						with self.subTest("""JIT Enabled"""	):
							a_      				=						model_jitted(**UpperCAmelCase	).to_tuple()

						with self.subTest("""JIT Disabled"""	):
							with jax.disable_jit():
								a_      				=						model_jitted(**UpperCAmelCase	).to_tuple()

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				for model_class_name in self.all_model_classes:
					a_      				=						model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""	)
					a_      				=						model(np.ones((1, 3, 2_24, 2_24)	)	)
					self.assertIsNotNone(UpperCAmelCase	)







def      UpperCamelCase_				(      ):
	a_      				=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
	return image







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




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




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""	)

				a_      				=						self.default_image_processor
				a_      				=						prepare_img()
				a_      				=						image_processor(images=UpperCAmelCase					,   return_tensors="""np"""	).pixel_values

				# prepare bool_masked_pos
				a_      				=						np.ones((1, 1_96)					,   dtype=UpperCAmelCase	)

				# forward pass
				a_      				=						model(pixel_values=UpperCAmelCase					,   bool_masked_pos=UpperCAmelCase	)
				a_      				=						outputs.logits

				# verify the logits
				a_      				=						(1, 1_96, 81_92)
				self.assertEqual(logits.shape					,   UpperCAmelCase	)

				a_      				=						np.array(
				    [[-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]]	)

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




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""	)

				a_      				=						self.default_image_processor
				a_      				=						prepare_img()
				a_      				=						image_processor(images=UpperCAmelCase					,   return_tensors="""np"""	)

				# forward pass
				a_      				=						model(**UpperCAmelCase	)
				a_      				=						outputs.logits

				# verify the logits
				a_      				=						(1, 10_00)
				self.assertEqual(logits.shape					,   UpperCAmelCase	)

				a_      				=						np.array([-1.23_85, -1.09_87, -1.01_08]	)

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

				a_      				=						2_81
				self.assertEqual(logits.argmax(-1	).item()					,   UpperCAmelCase	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""	)

				a_      				=						self.default_image_processor
				a_      				=						prepare_img()
				a_      				=						image_processor(images=UpperCAmelCase					,   return_tensors="""np"""	)

				# forward pass
				a_      				=						model(**UpperCAmelCase	)
				a_      				=						outputs.logits

				# verify the logits
				a_      				=						(1, 2_18_41)
				self.assertEqual(logits.shape					,   UpperCAmelCase	)

				a_      				=						np.array([1.68_81, -0.27_87, 0.59_01]	)

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

				a_      				=						23_96
				self.assertEqual(logits.argmax(-1	).item()					,   UpperCAmelCase	)


'''simple docstring'''
import enum
import shutil
import sys


lowercase__				, lowercase__											=shutil.get_terminal_size()

lowercase__											={'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}







class   a_ (      enum.Enum					):
			lowerCamelCase__	:			int       =    0
			lowerCamelCase__	:			Union[str, Any]       =    1







def      UpperCamelCase_				(      A__  ,      A__=""						):
	sys.stdout.write(str(A__						) + end						)
	sys.stdout.flush()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__=""						):
	forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''  ,      A__						)






def      UpperCamelCase_				(      ):
	forceWrite("""\r"""						)






def      UpperCamelCase_				(      A__  ,      A__						):
	forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''						)






def      UpperCamelCase_				(      ):
	forceWrite(""" """ * TERMINAL_WIDTH						)
	reset_cursor()






def      UpperCamelCase_				(      ):
	reset_cursor()
	forceWrite("""-""" * TERMINAL_WIDTH						)
