



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		,__lowerCamelCase		,__lowerCamelCase=3		,__lowerCamelCase=32		,__lowerCamelCase=3		,__lowerCamelCase=10		,__lowerCamelCase=[10, 20, 30, 40]		,__lowerCamelCase=[1, 1, 2, 1]		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase="relu"		,__lowerCamelCase=3		,__lowerCamelCase=None		,)					->      Any:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

						lowerCAmelCase__					:    Optional[Any]												=					None
						if self.use_labels:
										lowerCAmelCase__					:    Tuple												=					ids_tensor([self.batch_size]		,self.num_labels			)

						lowerCAmelCase__					:    Optional[Any]												=					self.get_config()

						return config, pixel_values, labels



		def 		lowerCAmelCase__				(self			)					->      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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					TFResNetModel(config=__A			)
						lowerCAmelCase__					:    Optional[int]												=					model(__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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.num_labels
						lowerCAmelCase__					:    Optional[int]												=					TFResNetForImageClassification(__A			)
						lowerCAmelCase__					:    int												=					model(__A		,labels=__A			)
						self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.prepare_config_and_inputs()
						lowerCAmelCase__					:    Union[str, Any]												=					config_and_inputs
						lowerCAmelCase__					:    Tuple												=					{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_tf
class 		lowerCamelCase__      (							A__				,    A__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		snake_case_     =(
		    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		snake_case_     =False
		snake_case_     =False
		snake_case_     =False
		snake_case_     =False
		snake_case_     =False



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					TFResNetModelTester(self			)
						lowerCAmelCase__					:    Union[str, Any]												=					ConfigTester(self		,config_class=__A		,has_text_modality=__A			)



		def 		lowerCAmelCase__				(self			)					->      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 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						return



		@unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						pass



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

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__					:    Union[str, Any]												=					model_class(__A			)
										lowerCAmelCase__					:    Tuple												=					inspect.signature(model.call			)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__					:    Optional[int]												=					[*signature.parameters.keys()]

										lowerCAmelCase__					:    Union[str, Any]												=					["""pixel_values"""]
										self.assertListEqual(arg_names[:1]		,__A			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						def check_hidden_states_output(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Any												=					model_class(__A			)
										lowerCAmelCase__					:    List[str]												=					model(**self._prepare_for_class(__A		,__A			)			)

										lowerCAmelCase__					:    Optional[int]												=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowerCAmelCase__					:    Optional[Any]												=					self.model_tester.num_stages
										self.assertEqual(len(__A			)		,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]		,)

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

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowerCAmelCase__					:    int												=					True

														check_hidden_states_output(__A		,__A		,__A			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__A			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__					:    List[Any]												=					TFResNetModel.from_pretrained(__A			)
										self.assertIsNotNone(__A			)






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image



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




		'''simple docstring'''



		@cached_property
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






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



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

						lowerCAmelCase__					:    Union[str, Any]												=					self.default_image_processor
						lowerCAmelCase__					:    Any												=					prepare_img()
						lowerCAmelCase__					:    Optional[Any]												=					image_processor(images=__A		,return_tensors='''tf'''			)

						# forward pass
						lowerCAmelCase__					:    Union[str, Any]												=					model(**__A			)

						# verify the logits
						lowerCAmelCase__					:    Tuple												=					tf.TensorShape((1, 10_00)			)
						self.assertEqual(outputs.logits.shape		,__A			)

						lowerCAmelCase__					:    List[Any]												=					tf.constant([-11.10_69, -9.7877, -8.3777]			)

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


if __name__ == "__main__":
							print(f"""{solution() = }""")

