


"""simple docstring"""


from __future__ import annotations

from typing import Any




def   snake_case		(     UpperCamelCase__       :						list[Any]    )   ->      None:
      create_state_space_tree(UpperCamelCase__			,				[]			,				0    )




def   snake_case		(     UpperCamelCase__       :						list[Any]			,				UpperCamelCase__       :						list[Any]			,				UpperCamelCase__       :						int    )   ->      None:

      if index == len(UpperCamelCase__    ):
            print(UpperCamelCase__    )
            return

      create_state_space_tree(UpperCamelCase__			,				UpperCamelCase__			,				index + 1    )
      current_subsequence.append(sequence[index]    )
      create_state_space_tree(UpperCamelCase__			,				UpperCamelCase__			,				index + 1    )
      current_subsequence.pop()


if __name__ == "__main__":
    __lowerCamelCase      :list[Any]	    =		[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(['A', 'B', 'C'])
    generate_all_subsequences(seq)








"""simple docstring"""


import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Optional[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=13     ,		__a:						Optional[Any]=32     ,		__a:						Dict=3     ,		__a:						int=4     ,		__a:						Dict=[10, 20, 30, 40]     ,		__a:						int=[2, 2, 3, 2]     ,		__a:						Any=True     ,		__a:						List[Any]=True     ,		__a:						Any=37     ,		__a:						Optional[int]="gelu"     ,		__a:						List[str]=10     ,		__a:						Optional[int]=0.02     ,		__a:						Dict=["stage2", "stage3", "stage4"]     ,		__a:						List[str]=[2, 3, 4]     ,		__a:						List[str]=None     ,		)->    Union[str, Any]:
           lowerCamelCase					:					Optional[int]        =	parent
           lowerCamelCase					:					Optional[int]        =	batch_size
           lowerCamelCase					:					Any        =	image_size
           lowerCamelCase					:					Tuple        =	num_channels
           lowerCamelCase					:					str        =	num_stages
           lowerCamelCase					:					List[str]        =	hidden_sizes
           lowerCamelCase					:					str        =	depths
           lowerCamelCase					:					Dict        =	is_training
           lowerCamelCase					:					Optional[Any]        =	use_labels
           lowerCamelCase					:					List[str]        =	intermediate_size
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					List[str]        =	num_labels
           lowerCamelCase					:					Union[str, Any]        =	initializer_range
           lowerCamelCase					:					List[Any]        =	out_features
           lowerCamelCase					:					Optional[Any]        =	out_indices
           lowerCamelCase					:					int        =	scope

     def 		a__						(	self:						str	)->    Optional[Any]:
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

           lowerCamelCase					:					Any        =	self.get_config()
           return config, pixel_values, labels

     def 		a__						(	self:						Dict	)->    Union[str, Any]:
           return ConvNextConfig(
               num_channels=self.num_channels     ,		hidden_sizes=self.hidden_sizes     ,		depths=self.depths     ,		num_stages=self.num_stages     ,		hidden_act=self.hidden_act     ,		is_decoder=__a     ,		initializer_range=self.initializer_range     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		num_labels=self.num_labels     ,		)

     def 		a__						(	self:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Any     ,		__a:						int	)->    List[Any]:
           lowerCamelCase					:					Optional[int]        =	ConvNextModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					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:						int     ,		__a:						Union[str, Any]     ,		__a:						List[Any]     ,		__a:						Tuple	)->    Optional[int]:
           lowerCamelCase					:					str        =	ConvNextForImageClassification(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)	)

     def 		a__						(	self:						List[Any]     ,		__a:						Any     ,		__a:						Optional[int]     ,		__a:						Tuple	)->    List[str]:
           lowerCamelCase					:					List[str]        =	ConvNextBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					int        =	model(__a	)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.hidden_sizes[1], 4, 4]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(model.channels     ,		config.hidden_sizes[1:]	)

           # verify backbone works with out_features=None
           lowerCamelCase					:					Tuple        =	None
           lowerCamelCase					:					List[str]        =	ConvNextBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[Any]        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.hidden_sizes[-1], 1, 1]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		1	)
           self.parent.assertListEqual(model.channels     ,		[config.hidden_sizes[-1]]	)







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


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					int													=(
         (
             ConvNextModel,
             ConvNextForImageClassification,
             ConvNextBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					str													=(
         {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
         if is_torch_available()
         else {}
     )

     snake_case__	:					Union[str, Any]													=True
     snake_case__	:					Optional[int]													=False
     snake_case__	:					Tuple													=False
     snake_case__	:					Union[str, Any]													=False
     snake_case__	:					Tuple													=False

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	ConvNextModelTester(self	)
           lowerCamelCase					:					List[Any]        =	ConfigTester(self     ,		config_class=__a     ,		has_text_modality=__a     ,		hidden_size=37	)

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

     @unittest.skip(reason="""ConvNext does not use inputs_embeds"""	)
     def 		a__						(	self:						int	)->    Dict:
           pass

     @unittest.skip(reason="""ConvNext does not support input and output embeddings"""	)
     def 		a__						(	self:						Dict	)->    Optional[Any]:
           pass

     @unittest.skip(reason="""ConvNext does not use feedforward chunking"""	)
     def 		a__						(	self:						int	)->    List[Any]:
           pass

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase					:					List[str]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						Optional[int]	)->    str:
           lowerCamelCase					:					Any        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						str	)->    int:
           lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__a	)

     def 		a__						(	self:						int	)->    Optional[int]:
           def check_hidden_states_output(__a:						Tuple     ,		__a:						int     ,		__a:						Tuple	):
                 lowerCamelCase					:					str        =	model_class(__a	)
                 model.to(__a	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase					:					Tuple        =	model(**self._prepare_for_class(__a     ,		__a	)	)

                 lowerCamelCase					:					int        =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowerCamelCase					:					Optional[int]        =	self.model_tester.num_stages
                 self.assertEqual(len(__a	)     ,		expected_num_stages + 1	)

                 # ConvNext'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     ,			lowerCamelCase					:					List[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					List[Any]        =	True
                 check_hidden_states_output(__a     ,		__a     ,		__a	)

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

                 check_hidden_states_output(__a     ,		__a     ,		__a	)

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           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[Any]	)->    Tuple:
           for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					str        =	ConvNextModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)




def   snake_case		(     )   ->      Optional[int]:
      lowerCamelCase					:					int        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
      return image


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


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Dict	)->    Union[str, Any]:
           return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						List[str]	)->    Dict:
           lowerCamelCase					:					Tuple        =	ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""	).to(__a	)

           lowerCamelCase					:					Dict        =	self.default_image_processor
           lowerCamelCase					:					Union[str, Any]        =	prepare_img()
           lowerCamelCase					:					Optional[Any]        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	model(**__a	)

           # verify the logits
           lowerCamelCase					:					Union[str, Any]        =	torch.Size((1, 1_000)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)

           lowerCamelCase					:					Tuple        =	torch.tensor([-0.02_60, -0.47_39, 0.19_11]	).to(__a	)

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


@require_torch
class 			A__	(						unittest.TestCase ,     __lowercase):


     """simple docstring"""
     snake_case__	:					Union[str, Any]													=(ConvNextBackbone,) if is_torch_available() else ()
     snake_case__	:					Optional[Any]													=ConvNextConfig

     snake_case__	:					Optional[Any]													=False

     def 		a__						(	self:						List[str]	)->    int:
           lowerCamelCase					:					Dict        =	ConvNextModelTester(self	)




