






from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					List[Any],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Union[str, Any]=3,						lowerCamelCase		:					Optional[int]=32,						lowerCamelCase		:					Optional[int]=3,						lowerCamelCase		:					int=10,						lowerCamelCase		:					Union[str, Any]=[10, 20, 30, 40],						lowerCamelCase		:					Dict=[1, 1, 2, 1],						lowerCamelCase		:					str=True,						lowerCamelCase		:					Tuple=True,						lowerCamelCase		:					Any="relu",						lowerCamelCase		:					Optional[int]=3,						lowerCamelCase		:					int=None,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    image_size
								lowercase__     				=    num_channels
								lowercase__     				=    embeddings_size
								lowercase__     				=    hidden_sizes
								lowercase__     				=    depths
								lowercase__     				=    is_training
								lowercase__     				=    use_labels
								lowercase__     				=    hidden_act
								lowercase__     				=    num_labels
								lowercase__     				=    scope
								lowercase__     				=    len(__A						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								lowercase__     				=    None
								if self.use_labels:
												lowercase__     				=    ids_tensor([self.batch_size],						self.num_labels						)

								lowercase__     				=    self.get_config()
								return config, pixel_values, labels




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								return RegNetConfig(
								    num_channels=self.num_channels,						embeddings_size=self.embeddings_size,						hidden_sizes=self.hidden_sizes,						depths=self.depths,						hidden_act=self.hidden_act,						num_labels=self.num_labels,						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    TFRegNetModel(config=__A						)
								lowercase__     				=    model(__A,						training=__A						)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    TFRegNetForImageClassification(__A						)
								lowercase__     				=    model(__A,						labels=__A,						training=__A						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								lowercase__     				=    config_and_inputs
								lowercase__     				=    {"pixel_values": pixel_values}
								return config, inputs_dict



@require_tf
class    _UpperCAmelCase      ( UpperCamelCase_	,UpperCamelCase_	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
				lowercase__       							= (
				    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    TFRegNetModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=__A,						has_text_modality=__A						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								return




				@unittest.skip(reason='''RegNet does not use inputs_embeds'''						)
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								pass




				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''						)						) == 0,						reason='''TF does not support backprop for grouped convolutions on CPU.''',						)
				@slow
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								super().test_keras_fit()




				@unittest.skip(reason='''RegNet does not support input and output embeddings'''						)
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

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

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




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__A						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase		:					List[str],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					str						):
												lowercase__     				=    model_class(__A						)
												lowercase__     				=    model(**self._prepare_for_class(__A,						__A						),						training=__A						)

												lowercase__     				=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowercase__     				=    self.model_tester.num_stages
												self.assertEqual(len(__A						),						expected_num_stages + 1						)

												# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]						),						[self.model_tester.image_size // 2, self.model_tester.image_size // 2],						)

								lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__     				=    ["basic", "bottleneck"]
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																lowercase__     				=    layer_type
																lowercase__     				=    True
																check_hidden_states_output(__A,						__A,						__A						)

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

																check_hidden_states_output(__A,						__A,						__A						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(lowerCamelCase		:					List[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Tuple={}						):
												lowercase__     				=    model(__A,						return_dict=__A,						**__A						)
												lowercase__     				=    model(__A,						return_dict=__A,						**__A						).to_tuple()

												def recursive_check(lowerCamelCase		:					Tuple,						lowerCamelCase		:					Dict						):
																if isinstance(__A,						(List, Tuple)						):
																				for tuple_iterable_value, dict_iterable_value in zip(__A,						__A						):
																								recursive_check(__A,						__A						)
																elif tuple_object is None:
																				return
																else:
																				self.assertTrue(
																				    all(tf.equal(__A,						__A						)						),						msg=(
																				        '''Tuple and dict output are not equal. Difference:'''
																				        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"""
																				    ),						)

																recursive_check(__A,						__A						)

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

												lowercase__     				=    self._prepare_for_class(__A,						__A						)
												lowercase__     				=    self._prepare_for_class(__A,						__A						)
												check_equivalence(__A,						__A,						__A						)

												lowercase__     				=    self._prepare_for_class(__A,						__A,						return_labels=__A						)
												lowercase__     				=    self._prepare_for_class(__A,						__A,						return_labels=__A						)
												check_equivalence(__A,						__A,						__A						)

												lowercase__     				=    self._prepare_for_class(__A,						__A						)
												lowercase__     				=    self._prepare_for_class(__A,						__A						)
												check_equivalence(__A,						__A,						__A,						{'''output_hidden_states''': True}						)

												lowercase__     				=    self._prepare_for_class(__A,						__A,						return_labels=__A						)
												lowercase__     				=    self._prepare_for_class(__A,						__A,						return_labels=__A						)
												check_equivalence(__A,						__A,						__A,						{'''output_hidden_states''': True}						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__A						)




				@slow
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    TFRegNetModel.from_pretrained(__A						)
												self.assertIsNotNone(__A						)






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image




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

				"""simple docstring"""




				@cached_property
				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								return (
								    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
								    if is_vision_available()
								    else None
								)




				@slow
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

								lowercase__     				=    self.default_image_processor
								lowercase__     				=    prepare_img()
								lowercase__     				=    image_processor(images=__A,						return_tensors='''tf'''						)

								# forward pass
								lowercase__     				=    model(**__A,						training=__A						)

								# verify the logits
								lowercase__     				=    tf.TensorShape((1, 1_000)						)
								self.assertEqual(outputs.logits.shape,						__A						)

								lowercase__     				=    tf.constant([-0.4180, -1.5051, -3.4836]						)

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












class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					str = "",						lowerCamelCase		:					bool = False						):

								'''simple docstring'''
								# Mapping from the first character of the prefix of the node
								lowercase__     				=    {}

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

								lowercase__     				=    prefix




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    0
								for q, w in zip(self.prefix,						lowerCamelCase						):
												if q != w:
																break

												x += 1

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




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					list[str]						):

								'''simple docstring'''
								for word in words:
												self.insert(lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								# Case 1: If the word is the prefix of the node
								# Solution: We set the current node as leaf
								if self.prefix == word:
												lowercase__     				=    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:
												lowercase__     				=    RadixNode(prefix=lowerCamelCase,						is_leaf=lowerCamelCase						)

								else:
												lowercase__     				=    self.nodes[word[0]]
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)

												# 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(lowerCamelCase						)

												# 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:
																lowercase__     				=    remaining_prefix

																lowercase__     				=    self.nodes[matching_string[0]]
																lowercase__     				=    RadixNode(lowerCamelCase,						lowerCamelCase						)
																lowercase__     				=    aux_node

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




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.nodes.get(word[0],						lowerCamelCase						)
								if not incoming_node:
												return False
								else:
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)
												# 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(lowerCamelCase						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.nodes.get(word[0],						lowerCamelCase						)
								if not incoming_node:
												return False
								else:
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)
												# 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(lowerCamelCase						)
												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:
																												lowercase__     				=    list(self.nodes.values()						)[0]
																												lowercase__     				=    merging_node.is_leaf
																												self.prefix += merging_node.prefix
																												lowercase__     				=    merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																				elif len(incoming_node.nodes						) > 1:
																								lowercase__     				=    False
																				# If there is 1 edge, we merge it with its child
																				else:
																								lowercase__     				=    list(incoming_node.nodes.values()						)[0]
																								lowercase__     				=    merging_node.is_leaf
																								incoming_node.prefix += merging_node.prefix
																								lowercase__     				=    merging_node.nodes

																				return True




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int = 0						):

								'''simple docstring'''
								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 						a    (		):






				'''simple docstring'''
				lowercase__     				=    '''banana bananas bandana band apple all beast'''.split()
				lowercase__     				=    RadixNode()
				root.insert_many(lowerCamelCase_       )

				assert all(root.find(lowerCamelCase_       ) 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 						a    (		):






				'''simple docstring'''
				assert test_trie()






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    RadixNode()
				lowercase__     				=    '''banana bananas bandanas bandana band apple all beast'''.split()
				root.insert_many(lowerCamelCase_       )

				print('''Words:'''	,     lowerCamelCase_       )
				print('''Tree:'''       )
				root.print_tree()


if __name__ == "__main__":
							main()




