



"""simple docstring"""







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        __A       :







		'''simple docstring'''





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






				"""simple docstring"""



				lowercase__   :    int				=			parent
				lowercase__   :    Optional[Any]				=			batch_size
				lowercase__   :    Optional[Any]				=			image_size
				lowercase__   :    Optional[Any]				=			num_channels
				lowercase__   :    Optional[Any]				=			embeddings_size
				lowercase__   :    Optional[Any]				=			hidden_sizes
				lowercase__   :    str				=			depths
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    List[Any]				=			use_labels
				lowercase__   :    Union[str, Any]				=			hidden_act
				lowercase__   :    Union[str, Any]				=			num_labels
				lowercase__   :    Tuple				=			scope
				lowercase__   :    Optional[Any]				=			len(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

				lowercase__   :    int				=			self.get_config()

				return config, pixel_values, labels







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""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 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  Optional[int]			,_snake_case						:  int			,_snake_case						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			TFResNetModel(config=_snake_case						)
				lowercase__   :    List[str]				=			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 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  Optional[Any]			,_snake_case						:  int			,_snake_case						:  Any						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Tuple				=			self.num_labels
				lowercase__   :    Union[str, Any]				=			TFResNetForImageClassification(_snake_case						)
				lowercase__   :    List[str]				=			model(_snake_case			,labels=_snake_case						)
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.prepare_config_and_inputs()
				lowercase__		,			lowercase__		,			lowercase__   :    Union[str, Any]				=			config_and_inputs
				lowercase__   :    Dict				=			{'''pixel_values''': pixel_values}
				return config, inputs_dict





@require_tf
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[int]   	=				(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		lowerCAmelCase			:					Any   	=				(
		    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					int   	=				False
		lowerCAmelCase			:					Union[str, Any]   	=				False
		lowerCAmelCase			:					List[str]   	=				False







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFResNetModelTester(self						)
				lowercase__   :    int				=			ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""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 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				return







		@unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''ResNet does not support input and output embeddings'''						)
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				pass







		def 					UpperCAmelCase    (	self						:  int						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Dict				=			self.model_tester.prepare_config_and_inputs_for_common()

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

						lowercase__   :    Any				=			['''pixel_values''']
						self.assertListEqual(arg_names[:1]			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				def check_hidden_states_output(_snake_case						:  Optional[int]			,_snake_case						:  List[str]			,_snake_case						:  Optional[Any]						):
						lowercase__   :    str				=			model_class(_snake_case						)
						lowercase__   :    Union[str, Any]				=			model(**self._prepare_for_class(_snake_case			,_snake_case						)						)

						lowercase__   :    List[Any]				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase__   :    Tuple				=			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]			,)

				lowercase__		,			lowercase__   :    int				=			self.model_tester.prepare_config_and_inputs_for_common()
				lowercase__   :    List[Any]				=			['''basic''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowercase__   :    List[Any]				=			layer_type
								lowercase__   :    Dict				=			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"]
								lowercase__   :    Dict				=			True

								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		int:






				"""simple docstring"""



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





def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    List[str]				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image





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







		'''simple docstring'''





		@cached_property
		def 					UpperCAmelCase    (	self						:  str						)					->		Any:






				"""simple docstring"""



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







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Tuple				=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

				lowercase__   :    Any				=			self.default_image_processor
				lowercase__   :    int				=			prepare_img()
				lowercase__   :    Tuple				=			image_processor(images=_snake_case			,return_tensors='''tf'''						)

				# forward pass
				lowercase__   :    Dict				=			model(**_snake_case						)

				# verify the logits
				lowercase__   :    List[str]				=			tf.TensorShape((1, 1_000)						)
				self.assertEqual(outputs.logits.shape			,_snake_case						)

				lowercase__   :    Any				=			tf.constant([-11.1069, -9.7877, -8.3777]						)

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







def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     bool:
    lowerCamelCase__    :      List[str]					 =		len(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]

    # for each arr value, a sum of zero(0) can be formed by not taking any element
    # hence True/1
    for i in range(arr_len + 1 ):
        lowerCamelCase__    :      Tuple					 =		True

    # sum is not zero and set is empty then false
    for i in range(1						,	required_sum + 1 ):
        lowerCamelCase__    :      Dict					 =		False

    for i in range(1						,	arr_len + 1 ):
        for j in range(1						,	required_sum + 1 ):
            if arr[i - 1] > j:
                lowerCamelCase__    :      str					 =		subset[i - 1][j]
            if arr[i - 1] <= j:
                lowerCamelCase__    :      Dict					 =		subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

    return subset[arr_len][required_sum]


if __name__ == "__main__":
    import doctest

    doctest.testmod()





