

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor

class   __UpperCamelCase      :




	def __init__(						self		:  str						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Optional[Any]=13						,		lowerCAmelCase		:  Tuple=32						,		lowerCAmelCase		:  Dict=2						,		lowerCAmelCase		:  List[Any]=3						,		lowerCAmelCase		:  Optional[int]=16						,		lowerCAmelCase		:  List[Any]=[1, 2, 1]						,		lowerCAmelCase		:  Any=[2, 2, 4]						,		lowerCAmelCase		:  str=2						,		lowerCAmelCase		:  List[Any]=2.0						,		lowerCAmelCase		:  Tuple=True						,		lowerCAmelCase		:  str=0.0						,		lowerCAmelCase		:  Any=0.0						,		lowerCAmelCase		:  List[Any]=0.1						,		lowerCAmelCase		:  List[str]="gelu"						,		lowerCAmelCase		:  str=False						,		lowerCAmelCase		:  List[str]=True						,		lowerCAmelCase		:  List[Any]=0.02						,		lowerCAmelCase		:  str=1e-5						,		lowerCAmelCase		:  str=True						,		lowerCAmelCase		:  Union[str, Any]=None						,		lowerCAmelCase		:  str=True						,		lowerCAmelCase		:  Any=10						,		lowerCAmelCase		:  Tuple=8						,		):


		'''simple docstring'''







		UpperCAmelCase_    			=     parent
		UpperCAmelCase_    			=     batch_size
		UpperCAmelCase_    			=     image_size
		UpperCAmelCase_    			=     patch_size
		UpperCAmelCase_    			=     num_channels
		UpperCAmelCase_    			=     embed_dim
		UpperCAmelCase_    			=     depths
		UpperCAmelCase_    			=     num_heads
		UpperCAmelCase_    			=     window_size
		UpperCAmelCase_    			=     mlp_ratio
		UpperCAmelCase_    			=     qkv_bias
		UpperCAmelCase_    			=     hidden_dropout_prob
		UpperCAmelCase_    			=     attention_probs_dropout_prob
		UpperCAmelCase_    			=     drop_path_rate
		UpperCAmelCase_    			=     hidden_act
		UpperCAmelCase_    			=     use_absolute_embeddings
		UpperCAmelCase_    			=     patch_norm
		UpperCAmelCase_    			=     layer_norm_eps
		UpperCAmelCase_    			=     initializer_range
		UpperCAmelCase_    			=     is_training
		UpperCAmelCase_    			=     scope
		UpperCAmelCase_    			=     use_labels
		UpperCAmelCase_    			=     type_sequence_label_size
		UpperCAmelCase_    			=     encoder_stride




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

		UpperCAmelCase_    			=     self.get_config()

		return config, pixel_values, labels




	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		return SwinConfig(
		    image_size=self.image_size						,		patch_size=self.patch_size						,		num_channels=self.num_channels						,		embed_dim=self.embed_dim						,		depths=self.depths						,		num_heads=self.num_heads						,		window_size=self.window_size						,		mlp_ratio=self.mlp_ratio						,		qkv_bias=self.qkv_bias						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		drop_path_rate=self.drop_path_rate						,		hidden_act=self.hidden_act						,		use_absolute_embeddings=self.use_absolute_embeddings						,		path_norm=self.patch_norm						,		layer_norm_eps=self.layer_norm_eps						,		initializer_range=self.initializer_range						,		encoder_stride=self.encoder_stride						,		)




	def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     TFSwinModel(config=lowerCAmelCase							)
		UpperCAmelCase_    			=     model(lowerCAmelCase							)

		UpperCAmelCase_    			=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
		UpperCAmelCase_    			=     int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

		self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, expected_seq_len, expected_dim)							)




	def  __A   (						self		:  Dict						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  Tuple						,		lowerCAmelCase		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     TFSwinForMaskedImageModeling(config=lowerCAmelCase							)
		UpperCAmelCase_    			=     model(lowerCAmelCase							)
		self.parent.assertEqual(
		    result.logits.shape						,		(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

		# test greyscale images
		UpperCAmelCase_    			=     1
		UpperCAmelCase_    			=     TFSwinForMaskedImageModeling(lowerCAmelCase							)

		UpperCAmelCase_    			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		UpperCAmelCase_    			=     model(lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape						,		(self.batch_size, 1, self.image_size, self.image_size)							)




	def  __A   (						self		:  Any						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.type_sequence_label_size
		UpperCAmelCase_    			=     TFSwinForImageClassification(lowerCAmelCase							)
		UpperCAmelCase_    			=     model(lowerCAmelCase						,		labels=lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.type_sequence_label_size)							)

		# test greyscale images
		UpperCAmelCase_    			=     1
		UpperCAmelCase_    			=     TFSwinForImageClassification(lowerCAmelCase							)
		UpperCAmelCase_    			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		UpperCAmelCase_    			=     model(lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.type_sequence_label_size)							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







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


@require_tf
class   __UpperCamelCase      (   lowercase       ,       lowercase       ,       unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       (
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__      			=       (
	    {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE__      			=       False
	SCREAMING_SNAKE_CASE__      			=       False
	SCREAMING_SNAKE_CASE__      			=       False
	SCREAMING_SNAKE_CASE__      			=       False




	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     TFSwinModelTester(self							)
		UpperCAmelCase_    			=     ConfigTester(self						,		config_class=lowerCAmelCase						,		embed_dim=37							)




	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()




	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		return




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase							)




	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase							)




	@unittest.skip(reason="Swin does not use inputs_embeds"							)
	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		pass




	def  __A   (						self		:  List[str]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()

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




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()

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

			UpperCAmelCase_    			=     ["pixel_values"]
			self.assertListEqual(arg_names[:1]						,		lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()
		UpperCAmelCase_    			=     True

		for model_class in self.all_model_classes:
			UpperCAmelCase_    			=     True
			UpperCAmelCase_    			=     False
			UpperCAmelCase_    			=     True
			UpperCAmelCase_    			=     model_class(lowerCAmelCase							)
			UpperCAmelCase_    			=     model(**self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)							)
			UpperCAmelCase_    			=     outputs.attentions
			UpperCAmelCase_    			=     len(self.model_tester.depths							)
			self.assertEqual(len(lowerCAmelCase							)						,		lowerCAmelCase							)

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			UpperCAmelCase_    			=     True
			UpperCAmelCase_    			=     config.window_size**2
			UpperCAmelCase_    			=     model_class(lowerCAmelCase							)
			UpperCAmelCase_    			=     model(**self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)							)
			UpperCAmelCase_    			=     outputs.attentions
			self.assertEqual(len(lowerCAmelCase							)						,		lowerCAmelCase							)

			self.assertListEqual(
			    list(attentions[0].shape[-3:]							)						,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,		)
			UpperCAmelCase_    			=     len(lowerCAmelCase							)

			# Check attention is always last and order is fine
			UpperCAmelCase_    			=     True
			UpperCAmelCase_    			=     True
			UpperCAmelCase_    			=     model_class(lowerCAmelCase							)
			UpperCAmelCase_    			=     model(**self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)							)

			if hasattr(self.model_tester						,		"num_hidden_states_types"							):
				UpperCAmelCase_    			=     self.model_tester.num_hidden_states_types
			else:
				# also another +1 for reshaped_hidden_states
				UpperCAmelCase_    			=     2
			self.assertEqual(out_len + added_hidden_states						,		len(lowerCAmelCase							)							)

			UpperCAmelCase_    			=     outputs.attentions

			self.assertEqual(len(lowerCAmelCase							)						,		lowerCAmelCase							)

			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]							)						,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,		)




	def  __A   (						self		:  str						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Union[str, Any]						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     model_class(lowerCAmelCase							)
		UpperCAmelCase_    			=     model(**self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)							)
		UpperCAmelCase_    			=     outputs.hidden_states

		UpperCAmelCase_    			=     getattr(
		    self.model_tester						,		"expected_num_hidden_layers"						,		len(self.model_tester.depths							) + 1							)
		self.assertEqual(len(lowerCAmelCase							)						,		lowerCAmelCase							)

		# Swin has a different seq_length
		UpperCAmelCase_    			=     to_atuple(config.patch_size							)

		UpperCAmelCase_    			=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

		self.assertListEqual(
		    list(hidden_states[0].shape[-2:]							)						,		[num_patches, self.model_tester.embed_dim]						,		)

		UpperCAmelCase_    			=     outputs.reshaped_hidden_states
		self.assertEqual(len(lowerCAmelCase							)						,		lowerCAmelCase							)

		UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     reshaped_hidden_states[0].shape

		UpperCAmelCase_    			=     tf.reshape(reshaped_hidden_states[0]						,		(batch_size, num_channels, height * width)							)
		UpperCAmelCase_    			=     tf.transpose(lowerCAmelCase						,		(0, 2, 1)							)

		self.assertListEqual(
		    list(reshaped_hidden_states.shape[-2:]							)						,		[num_patches, self.model_tester.embed_dim]						,		)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_    			=     to_atuple(self.model_tester.image_size							)

		for model_class in self.all_model_classes:
			UpperCAmelCase_    			=     True
			self.check_hidden_states_output(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCAmelCase_    			=     True

			self.check_hidden_states_output(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)




	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()
		UpperCAmelCase_    			=     3

		UpperCAmelCase_    			=     to_atuple(self.model_tester.image_size							)
		UpperCAmelCase_    			=     to_atuple(config.patch_size							)

		UpperCAmelCase_    			=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
		UpperCAmelCase_    			=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

		for model_class in self.all_model_classes:
			UpperCAmelCase_    			=     True
			self.check_hidden_states_output(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		(padded_height, padded_width)							)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCAmelCase_    			=     True
			self.check_hidden_states_output(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		(padded_height, padded_width)							)




	@slow
	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCAmelCase_    			=     TFSwinModel.from_pretrained(lowerCAmelCase							)
			self.assertIsNotNone(lowerCAmelCase							)


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




	@cached_property
	def  __A   (						self		:  int							):


		'''simple docstring'''







		return (
		    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							)
		    if is_vision_available()
		    else None
		)




	@slow
	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							)
		UpperCAmelCase_    			=     self.default_image_processor

		UpperCAmelCase_    			=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
		UpperCAmelCase_    			=     image_processor(images=lowerCAmelCase						,		return_tensors="tf"							)

		# forward pass
		UpperCAmelCase_    			=     model(lowerCAmelCase							)

		# verify the logits
		UpperCAmelCase_    			=     tf.TensorShape((1, 1_000)							)
		self.assertEqual(outputs.logits.shape						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     tf.constant([-0.0_948, -0.6_454, -0.0_921]							)
		self.assertTrue(np.allclose(outputs.logits[0, :3]						,		lowerCAmelCase						,		atol=1e-4							)							)


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class   __UpperCamelCase      :




	def __init__(						self		:  Optional[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     key
		UpperCAmelCase_    			=     freq




	def __str__(						self		:  List[str]							):


		'''simple docstring'''







		return F"Node(key={self.key}, freq={self.freq})"



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A    ,       A    ,       A      ):
	if i > j or i < 0 or j > len(A      ) - 1:
		return

	UpperCAmelCase_    			=     root[i][j]
	if parent == -1:  # root does not have a parent
		print(F"{key[node]} is the root of the binary search tree."      )
	elif is_left:
		print(F"{key[node]} is the left child of key {parent}."      )
	else:
		print(F"{key[node]} is the right child of key {parent}."      )

	print_binary_search_tree(A    ,       A    ,       A    ,       node - 1    ,       key[node]    ,       A      )
	print_binary_search_tree(A    ,       A    ,       node + 1    ,       A    ,       key[node]    ,       A      )



def  __lowerCAmelCase (  A      ):
	nodes.sort(key=lambda A      : node.key      )

	UpperCAmelCase_    			=     len(A      )

	UpperCAmelCase_    			=     [nodes[i].key for i in range(A      )]
	UpperCAmelCase_    			=     [nodes[i].freq for i in range(A      )]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# stores tree roots that will be used later for constructing binary search tree
	UpperCAmelCase_    			=     [[i if i == j else 0 for j in range(A      )] for i in range(A      )]

	for interval_length in range(2    ,       n + 1      ):
		for i in range(n - interval_length + 1      ):
			UpperCAmelCase_    			=     i + interval_length - 1

			UpperCAmelCase_    			=     sys.maxsize  # set the value to "infinity"
			UpperCAmelCase_    			=     total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]    ,       root[i + 1][j] + 1      ):  # r is a temporal root
				UpperCAmelCase_    			=     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				UpperCAmelCase_    			=     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				UpperCAmelCase_    			=     left + total[i][j] + right

				if dp[i][j] > cost:
					UpperCAmelCase_    			=     cost
					UpperCAmelCase_    			=     r

	print("Binary search tree nodes:"      )
	for node in nodes:
		print(A      )

	print(F"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."      )
	print_binary_search_tree(A    ,       A    ,       0    ,       n - 1    ,       -1    ,       A      )



def  __lowerCAmelCase (  ):
	# A sample binary search tree
	UpperCAmelCase_    			=     [Node(A    ,       randint(1    ,       50      )      ) for i in range(10    ,       0    ,       -1      )]
	find_optimal_binary_search_tree(A      )


if __name__ == "__main__":
				main()