




import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import EfficientNetForImageClassification, EfficientNetModel
	from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor

class 		lowercase_       :




					def __init__(   self:		Union[str, Any],     _lowercase:		Any,     _lowercase:		int=13,     _lowercase:		Dict=32,     _lowercase:		Dict=3,     _lowercase:		Dict=[3, 3, 5],     _lowercase:		Optional[int]=[32, 16, 24],     _lowercase:		Union[str, Any]=[16, 24, 40],     _lowercase:		Optional[int]=[1, 1, 2],     _lowercase:		Tuple=[1, 1, 2],     _lowercase:		List[Any]=[1, 6, 6],     _lowercase:		List[Any]=True,     _lowercase:		Optional[int]=True,     _lowercase:		Dict=37,     _lowercase:		Union[str, Any]="gelu",     _lowercase:		Tuple=10,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  kernel_sizes
									__lowerCAmelCase					=  in_channels
									__lowerCAmelCase					=  out_channels
									__lowerCAmelCase					=  strides
									__lowerCAmelCase					=  num_block_repeats
									__lowerCAmelCase					=  expand_ratios
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  num_labels
									__lowerCAmelCase					=  use_labels




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


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

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

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




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									return EfficientNetConfig(
									    num_channels=self.num_channels,     kernel_sizes=self.kernel_sizes,     in_channels=self.in_channels,     out_channels=self.out_channels,     strides=self.strides,     num_block_repeats=self.num_block_repeats,     expand_ratios=self.expand_ratios,     hidden_act=self.hidden_act,     num_labels=self.num_labels,     )




					def     _lowercase						(   self:		int,     _lowercase:		Optional[int],     _lowercase:		Any,     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  EfficientNetModel(config=_lowercase)
									model.to(_lowercase)
									model.eval()
									__lowerCAmelCase					=  model(_lowercase)
									# expected last hidden states: B, C, H // 4, W // 4
									self.parent.assertEqual(
									    result.last_hidden_state.shape,     (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4),     )




					def     _lowercase						(   self:		int,     _lowercase:		Optional[int],     _lowercase:		List[str],     _lowercase:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  EfficientNetForImageClassification(_lowercase)
									model.to(_lowercase)
									model.eval()
									__lowerCAmelCase					=  model(_lowercase,     labels=_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels))







					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


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





@require_torch
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
					__UpperCamelCase     	= (
					    {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  EfficientNetModelTester(self)
									__lowerCAmelCase					=  ConfigTester(
									    self,     config_class=_lowercase,     has_text_modality=_lowercase,     hidden_size=37)




					def     _lowercase						(   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     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return




					@unittest.skip(reason="""EfficientNet does not use inputs_embeds""")
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									pass




					@unittest.skip(reason="""EfficientNet does not support input and output embeddings""")
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									pass




					@unittest.skip(reason="""EfficientNet does not use feedforward chunking""")
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase					=  ["""pixel_values"""]
													self.assertListEqual(arg_names[:1],     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									def check_hidden_states_output(_lowercase:		Dict,     _lowercase:		str,     _lowercase:		Optional[int]):
													__lowerCAmelCase					=  model_class(_lowercase)
													model.to(_lowercase)
													model.eval()

													with torch.no_grad():
																	__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))

													__lowerCAmelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
													__lowerCAmelCase					=  sum(config.num_block_repeats) * 4
													self.assertEqual(len(_lowercase),     _lowercase)

													# EfficientNet'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],     )

									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)

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

													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)




					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									pass




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


									'''simple docstring'''


									for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase					=  EfficientNetModel.from_pretrained(_lowercase)
													self.assertIsNotNone(_lowercase)







					@is_pipeline_test
					@require_vision
					@slow
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									super().test_pipeline_image_classification()





def      UpperCAmelCase  (						)     ->     Dict:







				'''simple docstring'''


				__lowerCAmelCase					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				return image

@require_torch
@require_vision
class 		lowercase_       (	unittest.TestCase				):




					@cached_property
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									return AutoImageProcessor.from_pretrained("""google/efficientnet-b7""") if is_vision_available() else None







					@slow
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7""").to(_lowercase)

									__lowerCAmelCase					=  self.default_image_processor
									__lowerCAmelCase					=  prepare_img()
									__lowerCAmelCase					=  image_processor(images=_lowercase,     return_tensors="""pt""").to(_lowercase)

									# forward pass
									with torch.no_grad():
													__lowerCAmelCase					=  model(**_lowercase)

									# verify the logits
									__lowerCAmelCase					=  torch.Size((1, 1000))
									self.assertEqual(outputs.logits.shape,     _lowercase)

									__lowerCAmelCase					=  torch.tensor([0.0_001, 0.0_002, 0.0_002]).to(_lowercase)
									self.assertTrue(torch.allclose(outputs.logits[0, :3],     _lowercase,     atol=1e-4))











from hashlib import mda, shaaaa

__A						:		Optional[int]          =						(shaaaa, mda)

class 		lowercase_       :




					def __init__(   self:		str,     _lowercase:		int = 8):


									'''simple docstring'''


									__lowerCAmelCase					=  0b0
									__lowerCAmelCase					=  size




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


									'''simple docstring'''


									__lowerCAmelCase					=  self.hash_(_lowercase)
									self.bitarray |= h




					def     _lowercase						(   self:		Dict,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.hash_(_lowercase)
									return (h & self.bitarray) == h




					def __contains__(   self:		Dict,     _lowercase:		str):


									'''simple docstring'''


									return self.exists(_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  bin(_lowercase)[2:]
									return res.zfill(self.size)




					@property
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									return self.format_bin(self.bitarray)




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


									'''simple docstring'''


									__lowerCAmelCase					=  0b0
									for func in HASH_FUNCTIONS:
													__lowerCAmelCase					=  (
													    int.from_bytes(func(value.encode()).digest(),     """little""") % self.size
													)
													res |= 2**position
									return res




					def     _lowercase						(   self:		Any,     _lowercase:		str):


									'''simple docstring'''


									return self.format_bin(self.hash_(_lowercase))







					@property
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  bin(self.bitarray).count("""1""")
									return (n_ones / self.size) ** len(_lowercase)





