



"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
			    TFDeiTForImageClassification,
			    TFDeiTForImageClassificationWithTeacher,
			    TFDeiTForMaskedImageModeling,
			    TFDeiTModel,
			)
			from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor






class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	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__=2						,	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						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=2						,	)      ->	str:
				A__											=					parent
				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
				A__											=					scope
				A__											=					encoder_stride

				# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
				A__											=					(image_size // patch_size) ** 2
				A__											=					num_patches + 2


			def   snake_case__	(      self		)      ->	str:
				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__											=					self.get_config()

				return config, pixel_values, labels


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				return DeiTConfig(
				    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						,	encoder_stride=self.encoder_stride						,	)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					TFDeiTModel(config=SCREAMING_SNAKE_CASE__		)
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__		)
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(
				    result.reconstruction.shape						,	(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

				# test greyscale images
				A__											=					1
				A__											=					TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__		)

				A__											=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.reconstruction.shape						,	(self.batch_size, 1, self.image_size, self.image_size)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					self.type_sequence_label_size
				A__											=					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__		)
				A__											=					model(SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)		)

				# test greyscale images
				A__											=					1
				A__											=					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__		)

				A__											=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				A__											=					model(SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)		)







			def   snake_case__	(      self		)      ->	str:
				A__											=					self.prepare_config_and_inputs()
				A__							,							A__							,							A__											=					config_and_inputs
				A__											=					{"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Tuple							  =     (
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			A__	:					Tuple							  =     (
			    {
			        "feature-extraction": TFDeiTModel,
			        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			A__	:					Optional[int]							  =     False
			A__	:					Union[str, Any]							  =     False
			A__	:					Optional[Any]							  =     False
			A__	:					List[Any]							  =     False


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					TFDeiTModelTester(self		)
				A__											=					ConfigTester(self						,	config_class=SCREAMING_SNAKE_CASE__						,	has_text_modality=SCREAMING_SNAKE_CASE__						,	hidden_size=37		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				self.config_tester.run_common_tests()


			@unittest.skip(reason="DeiT does not use inputs_embeds"		)
			def   snake_case__	(      self		)      ->	Any:
				pass


			def   snake_case__	(      self		)      ->	List[Any]:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					A__											=					model_class(SCREAMING_SNAKE_CASE__		)
					self.assertIsInstance(model.get_input_embeddings()						,	(tf.keras.layers.Layer)		)
					A__											=					model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__						,	tf.keras.layers.Dense		)		)


			def   snake_case__	(      self		)      ->	int:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					A__											=					model_class(SCREAMING_SNAKE_CASE__		)
					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]						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	str:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=False		)      ->	List[str]:
				A__											=					super()._prepare_for_class(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	return_labels=SCREAMING_SNAKE_CASE__		)

				if return_labels:
					if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call		).parameters:
						del inputs_dict["labels"]

				return inputs_dict







			@slow
			def   snake_case__	(      self		)      ->	Optional[Any]:
				for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					A__											=					TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)



def       _lowerCamelCase				(							)	-> Optional[int]:






	"""simple docstring"""





	A__											=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
	return image






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

			"""simple docstring"""


			@cached_property
			def   snake_case__	(      self		)      ->	Union[str, Any]:
				return (
				    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"		)
				    if is_vision_available()
				    else None
				)







			@slow
			def   snake_case__	(      self		)      ->	str:
				A__											=					TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"		)

				A__											=					self.default_image_processor
				A__											=					prepare_img()
				A__											=					image_processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="tf"		)

				# forward pass
				A__											=					model(**SCREAMING_SNAKE_CASE__		)

				# verify the logits
				A__											=					tf.TensorShape((1, 1000)		)
				self.assertEqual(outputs.logits.shape						,	SCREAMING_SNAKE_CASE__		)

				A__											=					tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]		)

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









"""simple docstring"""







class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = ""						,	SCREAMING_SNAKE_CASE__ = False		)      ->	None:
				# Mapping from the first character of the prefix of the node
				A__											=					{}

				# A node will be a leaf if the tree contains its word
				A__											=					is_leaf

				A__											=					prefix


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	tuple[str, str, str]:
				A__											=					0
				for q, w in zip(self.prefix						,	SCREAMING_SNAKE_CASE__		):
					if q != w:
						break

					x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				for word in words:
					self.insert(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				# Case 1: If the word is the prefix of the node
				# Solution: We set the current node as leaf
				if self.prefix == word:
					A__											=					True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
					A__											=					RadixNode(prefix=SCREAMING_SNAKE_CASE__						,	is_leaf=SCREAMING_SNAKE_CASE__		)

				else:
					A__											=					self.nodes[word[0]]
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_SNAKE_CASE__		)

					# Case 3: The node prefix is equal to the matching
					# Solution: We insert remaining word on the next node
					if remaining_prefix == "":
						self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE__		)

					# Case 4: The word is greater equal to the matching
					# Solution: Create a node in between both nodes, change
					# prefixes and add the new node for the remaining word
					else:
						A__											=					remaining_prefix

						A__											=					self.nodes[matching_string[0]]
						A__											=					RadixNode(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
						A__											=					aux_node

						if remaining_word == "":
							A__											=					True
						else:
							self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	bool:
				A__											=					self.nodes.get(word[0]						,	SCREAMING_SNAKE_CASE__		)
				if not incoming_node:
					return False
				else:
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_SNAKE_CASE__		)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
						return False
					# This applies when the word and the prefix are equal
					elif remaining_word == "":
						return incoming_node.is_leaf
					# We have word remaining so we check the next node
					else:
						return incoming_node.find(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	bool:
				A__											=					self.nodes.get(word[0]						,	SCREAMING_SNAKE_CASE__		)
				if not incoming_node:
					return False
				else:
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_SNAKE_CASE__		)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
						return False
					# We have word remaining so we check the next node
					elif remaining_word != "":
						return incoming_node.delete(SCREAMING_SNAKE_CASE__		)
					else:
						# If it is not a leaf, we don't have to delete
						if not incoming_node.is_leaf:
							return False
						else:
							# We delete the nodes if no edges go from it
							if len(incoming_node.nodes		) == 0:
								del self.nodes[word[0]]
								# We merge the current node with its only child
								if len(self.nodes		) == 1 and not self.is_leaf:
									A__											=					list(self.nodes.values()		)[0]
									A__											=					merging_node.is_leaf
									self.prefix += merging_node.prefix
									A__											=					merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
							elif len(incoming_node.nodes		) > 1:
								A__											=					False
							# If there is 1 edge, we merge it with its child
							else:
								A__											=					list(incoming_node.nodes.values()		)[0]
								A__											=					merging_node.is_leaf
								incoming_node.prefix += merging_node.prefix
								A__											=					merging_node.nodes

							return True







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__ = 0		)      ->	None:
				if self.prefix != "":
					print("-" * height						,	self.prefix						,	"  (leaf)" if self.is_leaf else ""		)

				for value in self.nodes.values():
					value.print_tree(height + 1		)



def       _lowerCamelCase				(							)	-> bool:






	"""simple docstring"""





	A__											=					"banana bananas bandana band apple all beast".split()
	A__											=					RadixNode()
	root.insert_many(UpperCAmelCase_     )

	assert all(root.find(UpperCAmelCase_     ) for word in words     )
	assert not root.find("bandanas"     )
	assert not root.find("apps"     )
	root.delete("all"     )
	assert not root.find("all"     )
	root.delete("banana"     )
	assert not root.find("banana"     )
	assert root.find("bananas"     )

	return True

def       _lowerCamelCase				(							)	-> None:






	"""simple docstring"""





	assert test_trie()

def       _lowerCamelCase				(							)	-> None:






	"""simple docstring"""





	A__											=					RadixNode()
	A__											=					"banana bananas bandanas bandana band apple all beast".split()
	root.insert_many(UpperCAmelCase_     )

	print("Words:",	UpperCAmelCase_     )
	print("Tree:"     )
	root.print_tree()


if __name__ == "__main__":
			main()




