"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor



class      _UpperCAmelCase						:






    """simple docstring"""







    def __init__(			self    , _lowercase    , _lowercase=3    , _lowercase=32    , _lowercase=3    , _lowercase=10    , _lowercase=[10, 20, 30, 40]    , _lowercase=[1, 1, 2, 1]    , _lowercase=True    , _lowercase=True    , _lowercase="relu"    , _lowercase=3    , _lowercase=None    , )	->	int:
        _lowerCamelCase	: Dict       =       parent
        _lowerCamelCase	: Optional[int]       =       batch_size
        _lowerCamelCase	: Optional[int]       =       image_size
        _lowerCamelCase	: Union[str, Any]       =       num_channels
        _lowerCamelCase	: int       =       embeddings_size
        _lowerCamelCase	: int       =       hidden_sizes
        _lowerCamelCase	: Tuple       =       depths
        _lowerCamelCase	: List[str]       =       is_training
        _lowerCamelCase	: List[Any]       =       use_labels
        _lowerCamelCase	: Dict       =       hidden_act
        _lowerCamelCase	: str       =       num_labels
        _lowerCamelCase	: List[str]       =       scope
        _lowerCamelCase	: str       =       len(a__					)







    def 				a__					(			self					)	->	Dict:
        _lowerCamelCase	: str       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

        _lowerCamelCase	: str       =       None
        if self.use_labels:
            _lowerCamelCase	: List[str]       =       ids_tensor([self.batch_size]    , self.num_labels					)

        _lowerCamelCase	: str       =       self.get_config()

        return config, pixel_values, labels







    def 				a__					(			self					)	->	Optional[Any]:
        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 				a__					(			self    , _lowercase    , _lowercase    , _lowercase					)	->	List[str]:
        _lowerCamelCase	: List[str]       =       RegNetModel(config=a__					)
        model.to(a__					)
        model.eval()
        _lowerCamelCase	: List[Any]       =       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 				a__					(			self    , _lowercase    , _lowercase    , _lowercase					)	->	Union[str, Any]:
        _lowerCamelCase	: int       =       self.num_labels
        _lowerCamelCase	: Any       =       RegNetForImageClassification(a__					)
        model.to(a__					)
        model.eval()
        _lowerCamelCase	: List[str]       =       model(a__    , labels=a__					)
        self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels)					)







    def 				a__					(			self					)	->	Optional[int]:
        _lowerCamelCase	: List[str]       =       self.prepare_config_and_inputs()
        _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	: List[Any]       =       config_and_inputs
        _lowerCamelCase	: Optional[Any]       =       {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_torch
class      _UpperCAmelCase						( __snake_case ,			__snake_case ,			unittest.TestCase						):






    """simple docstring"""
    __snake_case    =       (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    __snake_case    =       (
        {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    __snake_case    =       False
    __snake_case    =       False
    __snake_case    =       False
    __snake_case    =       False







    def 				a__					(			self					)	->	List[Any]:
        _lowerCamelCase	: int       =       RegNetModelTester(self					)
        _lowerCamelCase	: Dict       =       ConfigTester(self    , config_class=a__    , has_text_modality=a__					)







    def 				a__					(			self					)	->	Optional[int]:
        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					)	->	str:
        return







    @unittest.skip(reason='''RegNet does not use inputs_embeds'''					)
    def 				a__					(			self					)	->	Any:
        pass







    @unittest.skip(reason='''RegNet does not support input and output embeddings'''					)
    def 				a__					(			self					)	->	str:
        pass







    def 				a__					(			self					)	->	Any:
        _lowerCamelCase,					_lowerCamelCase	: Any       =       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	: List[str]       =       inspect.signature(model.forward					)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _lowerCamelCase	: Optional[int]       =       [*signature.parameters.keys()]

            _lowerCamelCase	: Dict       =       ['''pixel_values''']
            self.assertListEqual(arg_names[:1]    , a__					)







    def 				a__					(			self					)	->	List[Any]:
        _lowerCamelCase	: Tuple       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a__					)







    def 				a__					(			self					)	->	Optional[int]:
        _lowerCamelCase,					_lowerCamelCase	: Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _lowerCamelCase	: int       =       model_class(config=a__					)
            for name, module in model.named_modules():
                if isinstance(a__    , (nn.BatchNormad, nn.GroupNorm)					):
                    self.assertTrue(
                        torch.all(module.weight == 1					)    , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    , )
                    self.assertTrue(
                        torch.all(module.bias == 0					)    , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    , )







    def 				a__					(			self					)	->	Any:
        def check_hidden_states_output(_lowercase    , _lowercase    , _lowercase					):
            _lowerCamelCase	: str       =       model_class(a__					)
            model.to(a__					)
            model.eval()

            with torch.no_grad():
                _lowerCamelCase	: Optional[int]       =       model(**self._prepare_for_class(a__    , a__					)					)

            _lowerCamelCase	: str       =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            _lowerCamelCase	: Dict       =       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]    , )

        _lowerCamelCase,					_lowerCamelCase	: Tuple       =       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	: Optional[int]       =       layer_type
                _lowerCamelCase	: int       =       True
                check_hidden_states_output(a__    , a__    , a__					)

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

                check_hidden_states_output(a__    , a__    , a__					)







    def 				a__					(			self					)	->	int:
        _lowerCamelCase	: List[str]       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*a__					)







    @slow
    def 				a__					(			self					)	->	Optional[int]:
        for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _lowerCamelCase	: List[str]       =       RegNetModel.from_pretrained(a__					)
            self.assertIsNotNone(a__					)





def 					UpperCamelCase					(       )      ->Optional[Any]:
    _lowerCamelCase	: Tuple       =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image



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






    """simple docstring"""







    @cached_property
    def 				a__					(			self					)	->	Optional[Any]:
        return (
            AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
            if is_vision_available()
            else None
        )







    @slow
    def 				a__					(			self					)	->	Optional[Any]:
        _lowerCamelCase	: List[Any]       =       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(a__					)

        _lowerCamelCase	: str       =       self.default_image_processor
        _lowerCamelCase	: List[str]       =       prepare_img()
        _lowerCamelCase	: Any       =       image_processor(images=a__    , return_tensors='''pt'''					).to(a__					)

        # forward pass
        with torch.no_grad():
            _lowerCamelCase	: int       =       model(**a__					)

        # verify the logits
        _lowerCamelCase	: Tuple       =       torch.Size((1, 1000)					)
        self.assertEqual(outputs.logits.shape    , a__					)

        _lowerCamelCase	: Optional[Any]       =       torch.tensor([-0.4180, -1.5051, -3.4836]					).to(a__					)

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






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:   Any						=						logging.get_logger(__name__)

_lowercase							:   Union[str, Any]						=						{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'efficientformer'

				def __init__(    self					, a__ = [3, 2, 6, 4]					, a__ = [48, 96, 224, 448]					, a__ = [True, True, True, True]					, a__ = 448					, a__ = 32					, a__ = 4					, a__ = 7					, a__ = 5					, a__ = 8					, a__ = 4					, a__ = 0.0					, a__ = 16					, a__ = 3					, a__ = 3					, a__ = 3					, a__ = 2					, a__ = 1					, a__ = 0.0					, a__ = 1					, a__ = True					, a__ = True					, a__ = 1e-5					, a__ = "gelu"					, a__ = 0.02					, a__ = 1e-12					, a__ = 224					, a__ = 1e-05					, **a__					, )						->					None:
					super().__init__(**a__ )

					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					hidden_sizes
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					initializer_range
					A             =					layer_norm_eps
					A             =					patch_size
					A             =					num_channels
					A             =					depths
					A             =					mlp_expansion_ratio
					A             =					downsamples
					A             =					dim
					A             =					key_dim
					A             =					attention_ratio
					A             =					resolution
					A             =					pool_size
					A             =					downsample_patch_size
					A             =					downsample_stride
					A             =					downsample_pad
					A             =					drop_path_rate
					A             =					num_metaad_blocks
					A             =					distillation
					A             =					use_layer_scale
					A             =					layer_scale_init_value
					A             =					image_size
					A             =					batch_norm_eps



