



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
							from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class   __lowerCAmelCase  :
					"""simple docstring"""






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






								"""simple docstring"""


								A__	  =     parent
								A__	  =     batch_size
								A__	  =     image_size
								A__	  =     num_channels
								A__	  =     embeddings_size
								A__	  =     hidden_sizes
								A__	  =     depths
								A__	  =     is_training
								A__	  =     use_labels
								A__	  =     hidden_act
								A__	  =     num_labels
								A__	  =     scope
								A__	  =     len(_snake_case       )



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






								"""simple docstring"""


								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.num_labels       )

								A__	  =     self.get_config()

								return config, pixel_values, labels



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								return ResNetConfig(
								    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					,       image_size=self.image_size					,       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Tuple					,       _snake_case       :							str					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     TFResNetModel(config=_snake_case       )
								A__	  =     model(_snake_case       )
								# 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      _a				(  self       :							List[str]					,       _snake_case       :							Optional[int]					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.num_labels
								A__	  =     TFResNetForImageClassification(_snake_case       )
								A__	  =     model(_snake_case					,       labels=_snake_case       )
								self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.prepare_config_and_inputs()
								A__  ,			A__  ,			A__	  =     config_and_inputs
								A__	  =     {'pixel_values': pixel_values}
								return config, inputs_dict







@require_tf
class   __lowerCAmelCase  (		UpperCAmelCase_				,  UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Any      							=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					A__					:		List[str]      							=     (
					    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__					:		List[str]      							=     False
					A__					:		str      							=     False
					A__					:		List[Any]      							=     False
					A__					:		Tuple      							=     False
					A__					:		str      							=     False



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     TFResNetModelTester(self       )
								A__	  =     ConfigTester(self					,       config_class=_snake_case					,       has_text_modality=_snake_case       )



					def      _a				(  self       :							Any       ):






								"""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       :							Dict       ):






								"""simple docstring"""


								return



					@unittest.skip(reason='ResNet does not use inputs_embeds'       )
					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ResNet does not support input and output embeddings'       )
					def      _a				(  self       :							str       ):






								"""simple docstring"""


								pass



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






								"""simple docstring"""


								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

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



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case       :							Dict					,       _snake_case       :							List[Any]					,       _snake_case       :							str       ):
											A__	  =     model_class(_snake_case       )
											A__	  =     model(**self._prepare_for_class(_snake_case					,       _snake_case       )       )

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

											A__	  =     self.model_tester.num_stages
											self.assertEqual(len(_snake_case       )					,       expected_num_stages + 1       )

											# ResNet'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 // 4, self.model_tester.image_size // 4]					,       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()
								A__	  =     ['basic', 'bottleneck']
								for model_class in self.all_model_classes:
											for layer_type in layers_type:
														A__	  =     layer_type
														A__	  =     True
														check_hidden_states_output(_snake_case					,       _snake_case					,       _snake_case       )

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

														check_hidden_states_output(_snake_case					,       _snake_case					,       _snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case       )



					@slow
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__	  =     TFResNetModel.from_pretrained(_snake_case       )
											self.assertIsNotNone(_snake_case       )







def A    (							)    ->   str:
			A__	  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
			return image






@require_tf
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					@cached_property
					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
								    if is_vision_available()
								    else None
								)



					@slow
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

								A__	  =     self.default_image_processor
								A__	  =     prepare_img()
								A__	  =     image_processor(images=_snake_case					,       return_tensors='tf'       )

								# forward pass
								A__	  =     model(**_snake_case       )

								# verify the logits
								A__	  =     tf.TensorShape((1, 10_00)       )
								self.assertEqual(outputs.logits.shape					,       _snake_case       )

								A__	  =     tf.constant([-11.1069, -9.7877, -8.3777]       )

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








'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

    def _delete(__lowerCamelCase							:	TrieNode			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int ) -> bool:
       if index == len(__lowerCamelCase ):
          # If word does not exist
          if not curr.is_leaf:
             return False
          UpperCAmelCase__	:int      =      False
          return len(curr.nodes ) == 0
       UpperCAmelCase__	:str      =      word[index]
       UpperCAmelCase__	:Optional[Any]      =      curr.nodes.get(__lowerCamelCase )
       # If char not in current trie node
       if not char_node:
          return False
       # Flag to check if node can be deleted
       UpperCAmelCase__	:Any      =      _delete(__lowerCamelCase			,       __lowerCamelCase			,       index + 1 )
       if delete_curr:
          del curr.nodes[char]
          return len(curr.nodes ) == 0
       return delete_curr

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

   for key, value in node.nodes.items():
      print_words(UpperCamelCase_,   word + key						)


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) for word in words						)
   assert root.find('''banana'''						)
   assert not root.find('''bandanas'''						)
   assert not root.find('''apps'''						)
   assert root.find('''apple'''						)
   assert root.find('''all'''						)
   root.delete('''all'''						)
   assert not root.find('''all'''						)
   root.delete('''banana'''						)
   assert not root.find('''banana'''						)
   assert root.find('''bananas'''						)
   return True


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



