

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor






class      lowerCamelCase_      :




	def __init__(     self						:     str					,      __A						:     Tuple					,      __A						:     Union[str, Any]=13					,      __A						:     Union[str, Any]=30					,      __A						:     Optional[int]=2					,      __A						:     Optional[Any]=3					,      __A						:     Optional[Any]=True					,      __A						:     str=True					,      __A						:     Dict=32					,      __A						:     List[Any]=2					,      __A						:     Dict=4					,      __A						:     str=37					,      __A						:     Union[str, Any]="gelu"					,      __A						:     Optional[Any]=0.1					,      __A						:     Dict=0.1					,      __A						:     List[str]=10					,      __A						:     Optional[Any]=0.0_2					,      __A						:     Optional[int]=3					,      __A						:     Tuple=0.6					,      __A						:     Any=None					,      ):
		__A :			int		    =			parent
		__A :			str		    =			batch_size
		__A :			List[Any]		    =			image_size
		__A :			Optional[int]		    =			patch_size
		__A :			int		    =			num_channels
		__A :			Tuple		    =			is_training
		__A :			Optional[int]		    =			use_labels
		__A :			Union[str, Any]		    =			hidden_size
		__A :			Dict		    =			num_hidden_layers
		__A :			Dict		    =			num_attention_heads
		__A :			int		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Dict		    =			hidden_dropout_prob
		__A :			List[str]		    =			attention_probs_dropout_prob
		__A :			Union[str, Any]		    =			type_sequence_label_size
		__A :			str		    =			initializer_range
		__A :			Any		    =			mask_ratio
		__A :			int		    =			scope

		# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
		# (we add 1 for the [CLS] token)
		__A :			str		    =			(image_size // patch_size) ** 2
		__A :			List[Any]		    =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[int]		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		__A :			Union[str, Any]		    =			None
		if self.use_labels:
			__A :			Union[str, Any]		    =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)

		__A :			Union[str, Any]		    =			self.get_config()

		return config, pixel_values, labels




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		return ViTMAEConfig(
		    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					,      decoder_hidden_size=self.hidden_size					,      decoder_num_hidden_layers=self.num_hidden_layers					,      decoder_num_attention_heads=self.num_attention_heads					,      decoder_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					,      mask_ratio=self.mask_ratio					,      )




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     Tuple					,      __A						:     int					,      __A						:     Any							):
		__A :			int		    =			TFViTMAEModel(config=__A							)
		__A :			List[str]		    =			model(__A					,      training=__A							)
		self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     str					,      __A						:     Union[str, Any]					,      __A						:     Union[str, Any]							):
		__A :			Optional[Any]		    =			TFViTMAEForPreTraining(__A							)
		__A :			Optional[Any]		    =			model(__A					,      training=__A							)
		# expected sequence length = num_patches
		__A :			Any		    =			(self.image_size // self.patch_size) ** 2
		__A :			str		    =			self.patch_size**2 * self.num_channels
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)							)

		# test greyscale images
		__A :			Tuple		    =			1
		__A :			Optional[int]		    =			TFViTMAEForPreTraining(__A							)

		__A :			List[Any]		    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		__A :			List[str]		    =			model(__A					,      training=__A							)
		__A :			List[str]		    =			self.patch_size**2
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			int		    =			self.prepare_config_and_inputs()
		((__A)    ,				(__A)    ,				(__A)) :			int		    =			config_and_inputs
		__A :			Optional[Any]		    =			{"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_tf
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Any     					=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
	_lowercase		:   Optional[Any]     					=				{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

	_lowercase		:   Optional[int]     					=				False
	_lowercase		:   Optional[int]     					=				False
	_lowercase		:   Tuple     					=				False
	_lowercase		:   List[str]     					=				False




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			Optional[Any]		    =			TFViTMAEModelTester(self							)
		__A :			Optional[Any]		    =			ConfigTester(self					,      config_class=__A					,      has_text_modality=__A					,      hidden_size=37							)




	def        lowerCAmelCase_       (     self						:     int							):
		self.config_tester.run_common_tests()




	@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
	def        lowerCAmelCase_       (     self						:     List[str]							):
		pass




	def        lowerCAmelCase_       (     self						:     str							):
		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			Optional[int]		    =			model_class(__A							)
			self.assertIsInstance(model.get_input_embeddings()					,      (tf.keras.layers.Layer)							)
			__A :			Dict		    =			model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__A					,      tf.keras.layers.Layer							)							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A    ,				__A :			Optional[int]		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

			__A :			str		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Optional[int]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_pretraining(*__A							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		# make the mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			Union[str, Any]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Dict		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			__A :			str		    =			model_class(__A							)
			__A :			int		    =			self._prepare_for_class(__A					,      __A							)

			__A :			str		    =			model(__A					,      noise=__A							)

			__A :			Optional[int]		    =			copy.deepcopy(self._prepare_for_class(__A					,      __A							)							)
			__A :			Optional[Any]		    =			model(**__A					,      noise=__A							)
			__A :			int		    =			outputs_dict[0].numpy()
			__A :			Union[str, Any]		    =			outputs_keywords[0].numpy()

			self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)					,      1e-6							)




	def        lowerCAmelCase_       (     self						:     int							):
		# make the mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			str		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Union[str, Any]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		def prepare_numpy_arrays(__A						:     Union[str, Any]							):
			__A :			Union[str, Any]		    =			{}
			for k, v in inputs_dict.items():
				if tf.is_tensor(__A							):
					__A :			Dict		    =			v.numpy()
				else:
					__A :			str		    =			np.array(__A							)

			return inputs_np_dict

		for model_class in self.all_model_classes:
			__A :			Dict		    =			model_class(__A							)

			__A :			Optional[Any]		    =			self._prepare_for_class(__A					,      __A							)
			__A :			Union[str, Any]		    =			prepare_numpy_arrays(__A							)

			__A :			List[str]		    =			model(__A					,      noise=__A							)
			__A :			Union[str, Any]		    =			model(**__A					,      noise=__A							)
			self.assert_outputs_same(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Union[str, Any]					,      __A						:     Union[str, Any]					,      __A						:     Dict							):
		# make masks reproducible
		np.random.seed(2							)

		__A :			Tuple		    =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
		__A :			List[str]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
		__A :			Optional[Any]		    =			tf.constant(__A							)

		# Add `noise` argument.
		# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
		__A :			List[Any]		    =			tf_noise

		super().check_pt_tf_models(__A					,      __A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		# make mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			Union[str, Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			Optional[int]		    =			{
		    module_member
		    for model_class in self.all_model_classes
		    for module in (import_module(model_class.__module__							),)
		    for module_member_name in dir(__A							)
		    if module_member_name.endswith("""MainLayer"""							)
		    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
		    and module_member_name[: -len("""MainLayer"""							)] == model_class.__name__[: -len("""Model"""							)]
		    for module_member in (getattr(__A					,      __A							),)
		    if isinstance(__A					,      __A							)
		    and tf.keras.layers.Layer in module_member.__bases__
		    and getattr(__A					,      """_keras_serializable"""					,      __A							)
		}

		__A :			Optional[int]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Optional[Any]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
		__A :			List[str]		    =			tf.convert_to_tensor(__A							)
		inputs_dict.update({"""noise""": noise}							)

		for main_layer_class in tf_main_layer_classes:
			__A :			Union[str, Any]		    =			main_layer_class(__A							)

			__A :			Tuple		    =			{
			    name: tf.keras.Input(tensor.shape[1:]					,      dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
			}

			__A :			List[str]		    =			tf.keras.Model(__A					,      outputs=main_layer(__A							)							)
			__A :			Union[str, Any]		    =			model(__A							)

			with tempfile.TemporaryDirectory() as tmpdirname:
				__A :			List[str]		    =			os.path.join(__A					,      """keras_model.h5"""							)
				model.save(__A							)
				__A :			List[Any]		    =			tf.keras.models.load_model(
				    __A					,      custom_objects={main_layer_class.__name__: main_layer_class}							)
				assert isinstance(__A					,      tf.keras.Model							)
				__A :			str		    =			model(__A							)
				self.assert_outputs_same(__A					,      __A							)




	@slow
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		# make mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			List[str]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Optional[int]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			__A :			Any		    =			model_class(__A							)
			__A :			Dict		    =			self._prepare_for_class(__A					,      __A							)
			__A :			Optional[Any]		    =			model(__A					,      noise=__A							)

			if model_class.__name__ == "TFViTMAEModel":
				__A :			List[str]		    =			outputs.last_hidden_state.numpy()
				__A :			Optional[Any]		    =			0
			else:
				__A :			List[str]		    =			outputs.logits.numpy()
				__A :			List[str]		    =			0

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(__A					,      saved_model=__A							)
				__A :			List[str]		    =			model_class.from_pretrained(__A							)
				__A :			str		    =			model(__A					,      noise=__A							)

				if model_class.__name__ == "TFViTMAEModel":
					__A :			Dict		    =			after_outputs["""last_hidden_state"""].numpy()
					__A :			List[str]		    =			0
				else:
					__A :			Optional[int]		    =			after_outputs["""logits"""].numpy()
					__A :			Dict		    =			0

				__A :			Optional[int]		    =			np.amax(np.abs(out_a - out_a							)							)
				self.assertLessEqual(__A					,      1e-5							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		# make mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			List[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			List[str]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Tuple		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			__A :			Tuple		    =			model_class(__A							)
			__A :			Optional[int]		    =			self._prepare_for_class(__A					,      __A							)

			__A :			Tuple		    =			model(__A					,      noise=__A							)
			__A :			int		    =			model.get_config()
			# make sure that returned config is jsonifiable, which is required by keras
			json.dumps(__A							)
			__A :			Dict		    =			model_class.from_config(model.get_config()							)
			# make sure it also accepts a normal config
			__A :			Tuple		    =			model_class.from_config(model.config							)
			__A :			Union[str, Any]		    =			new_model(__A							)  # Build model
			new_model.set_weights(model.get_weights()							)
			__A :			Optional[int]		    =			new_model(__A					,      noise=__A							)

			self.assert_outputs_same(__A					,      __A							)




	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		pass




	@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
	def        lowerCAmelCase_       (     self						:     int							):
		pass




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Dict		    =			TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""							)
		self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				Optional[Any]:
	__A :			Any		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image







@require_tf
@require_vision
class      lowerCamelCase_      (	unittest.TestCase	):




	@cached_property
	def        lowerCAmelCase_       (     self						:     int							):
		return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None




	@slow
	def        lowerCAmelCase_       (     self						:     int							):
		# make random mask reproducible across the PT and TF model
		np.random.seed(2							)

		__A :			List[Any]		    =			TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							)

		__A :			List[Any]		    =			self.default_image_processor
		__A :			Union[str, Any]		    =			prepare_img()
		__A :			Union[str, Any]		    =			image_processor(images=__A					,      return_tensors="""tf"""							)

		# prepare a noise vector that will be also used for testing the TF model
		# (this way we can ensure that the PT and TF models operate on the same inputs)
		__A :			Dict		    =			ViTMAEConfig()
		__A :			List[str]		    =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
		__A :			List[Any]		    =			np.random.uniform(size=(1, num_patches)							)

		# forward pass
		__A :			int		    =			model(**__A					,      noise=__A							)

		# verify the logits
		__A :			Optional[Any]		    =			tf.convert_to_tensor([1, 196, 768]							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Optional[Any]		    =			tf.convert_to_tensor(
		    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]							)

		tf.debugging.assert_near(outputs.logits[0, :3, :3]					,      __A					,      atol=1e-4							)








from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





