
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			):






		def __init__(		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=1_00							,			SCREAMING_SNAKE_CASE__		:							List[str]=13							,			SCREAMING_SNAKE_CASE__		:							List[Any]=30							,			SCREAMING_SNAKE_CASE__		:							int=2							,			SCREAMING_SNAKE_CASE__		:							int=3							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=True							,			SCREAMING_SNAKE_CASE__		:							List[str]=True							,			SCREAMING_SNAKE_CASE__		:							Any=32							,			SCREAMING_SNAKE_CASE__		:							List[str]=5							,			SCREAMING_SNAKE_CASE__		:							Tuple=4							,			SCREAMING_SNAKE_CASE__		:							Any=37							,			SCREAMING_SNAKE_CASE__		:							List[Any]="gelu"							,			SCREAMING_SNAKE_CASE__		:							int=0.1							,			SCREAMING_SNAKE_CASE__		:							Dict=0.1							,			SCREAMING_SNAKE_CASE__		:							int=10							,			SCREAMING_SNAKE_CASE__		:							Dict=0.02							,			SCREAMING_SNAKE_CASE__		:							Dict=3							,			)     ->							Dict:
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	vocab_size
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	patch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	is_training
						__lowerCamelCase    					=	use_labels
						__lowerCamelCase    					=	hidden_size
						__lowerCamelCase    					=	num_hidden_layers
						__lowerCamelCase    					=	num_attention_heads
						__lowerCamelCase    					=	intermediate_size
						__lowerCamelCase    					=	hidden_act
						__lowerCamelCase    					=	hidden_dropout_prob
						__lowerCamelCase    					=	attention_probs_dropout_prob
						__lowerCamelCase    					=	type_sequence_label_size
						__lowerCamelCase    					=	initializer_range

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






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

						__lowerCamelCase    					=	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		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]  )     ->							Dict:
						__lowerCamelCase    					=	FlaxBeitModel(config=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)  )






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							List[Any]:
						__lowerCamelCase    					=	FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length - 1, self.vocab_size)  )






		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							List[Any]:
						__lowerCamelCase    					=	self.type_sequence_label_size
						__lowerCamelCase    					=	FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)  )

						# test greyscale images
						__lowerCamelCase    					=	1
						__lowerCamelCase    					=	FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )







		def  __A       (		self		:							Tuple  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	self.prepare_config_and_inputs()
						(
						    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    
						)    					=	config_and_inputs
						__lowerCamelCase    					=	{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_flax
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						str        =		(
		    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
		)






		def  __A       (		self		:							int  )     ->							None:
						__lowerCamelCase    					=	FlaxBeitModelTester(self  )
						__lowerCamelCase    					=	ConfigTester(self							,			config_class=SCREAMING_SNAKE_CASE__							,			has_text_modality=SCREAMING_SNAKE_CASE__							,			hidden_size=37  )






		def  __A       (		self		:							Optional[int]  )     ->							List[Any]:
						self.config_tester.run_common_tests()






		def  __A       (		self		:							List[Any]  )     ->							List[str]:
						__lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()

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

										__lowerCamelCase    					=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__  ):
														__lowerCamelCase    					=	self._prepare_for_class(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														__lowerCamelCase    					=	model_class(SCREAMING_SNAKE_CASE__  )

														@jax.jit
														def model_jitted(SCREAMING_SNAKE_CASE__		:							str							,			**SCREAMING_SNAKE_CASE__		:							Dict  ):
																		return model(pixel_values=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )

														with self.subTest('''JIT Enabled'''  ):
																		__lowerCamelCase    					=	model_jitted(**SCREAMING_SNAKE_CASE__  ).to_tuple()

														with self.subTest('''JIT Disabled'''  ):
																		with jax.disable_jit():
																						__lowerCamelCase    					=	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		:							Any  )     ->							Dict:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							Any:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]  )     ->							Tuple:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__  )







		@slow
		def  __A       (		self		:							List[Any]  )     ->							Optional[int]:
						for model_class_name in self.all_model_classes:
										__lowerCamelCase    					=	model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''  )
										__lowerCamelCase    					=	model(np.ones((1, 3, 2_24, 2_24)  )  )
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )





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







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






		@cached_property
		def  __A       (		self		:							List[str]  )     ->							Any:
						return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''  ) if is_vision_available() else None






		@slow
		def  __A       (		self		:							int  )     ->							Optional[int]:
						__lowerCamelCase    					=	FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''  )

						__lowerCamelCase    					=	self.default_image_processor
						__lowerCamelCase    					=	prepare_img()
						__lowerCamelCase    					=	image_processor(images=SCREAMING_SNAKE_CASE__							,			return_tensors='''np'''  ).pixel_values

						# prepare bool_masked_pos
						__lowerCamelCase    					=	np.ones((1, 1_96)							,			dtype=SCREAMING_SNAKE_CASE__  )

						# forward pass
						__lowerCamelCase    					=	model(pixel_values=SCREAMING_SNAKE_CASE__							,			bool_masked_pos=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	outputs.logits

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

						__lowerCamelCase    					=	np.array(
						    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]  )

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






		@slow
		def  __A       (		self		:							int  )     ->							Optional[int]:
						__lowerCamelCase    					=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''  )

						__lowerCamelCase    					=	self.default_image_processor
						__lowerCamelCase    					=	prepare_img()
						__lowerCamelCase    					=	image_processor(images=SCREAMING_SNAKE_CASE__							,			return_tensors='''np'''  )

						# forward pass
						__lowerCamelCase    					=	model(**SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	outputs.logits

						# verify the logits
						__lowerCamelCase    					=	(1, 10_00)
						self.assertEqual(logits.shape							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	np.array([-1.2385, -1.0987, -1.0108]  )

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

						__lowerCamelCase    					=	2_81
						self.assertEqual(logits.argmax(-1  ).item()							,			SCREAMING_SNAKE_CASE__  )







		@slow
		def  __A       (		self		:							Dict  )     ->							int:
						__lowerCamelCase    					=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''  )

						__lowerCamelCase    					=	self.default_image_processor
						__lowerCamelCase    					=	prepare_img()
						__lowerCamelCase    					=	image_processor(images=SCREAMING_SNAKE_CASE__							,			return_tensors='''np'''  )

						# forward pass
						__lowerCamelCase    					=	model(**SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	outputs.logits

						# verify the logits
						__lowerCamelCase    					=	(1, 2_18_41)
						self.assertEqual(logits.shape							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	np.array([1.6881, -0.2787, 0.5901]  )

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

						__lowerCamelCase    					=	23_96
						self.assertEqual(logits.argmax(-1  ).item()							,			SCREAMING_SNAKE_CASE__  )





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



