
"""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 __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Union[str, Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Tuple=13							, _snake_case    :       List[Any]=32							, _snake_case    :       Optional[Any]=3							, _snake_case    :       Optional[int]=4							, _snake_case    :       Dict=[10, 20, 30, 40]							, _snake_case    :       Optional[int]=[2, 2, 3, 2]							, _snake_case    :       List[Any]=True							, _snake_case    :       Union[str, Any]=True							, _snake_case    :       List[Any]=37							, _snake_case    :       int="gelu"							, _snake_case    :       Optional[Any]=10							, _snake_case    :       Optional[int]=0.0_2							, _snake_case    :       List[Any]=["stage2", "stage3", "stage4"]							, _snake_case    :       Optional[int]=[2, 3, 4]							, _snake_case    :       str=None							, )   ->			Dict:







								"""simple docstring"""
								A_								=    parent
								A_								=    batch_size
								A_								=    image_size
								A_								=    num_channels
								A_								=    num_stages
								A_								=    hidden_sizes
								A_								=    depths
								A_								=    is_training
								A_								=    use_labels
								A_								=    intermediate_size
								A_								=    hidden_act
								A_								=    num_labels
								A_								=    initializer_range
								A_								=    out_features
								A_								=    out_indices
								A_								=    scope




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







								"""simple docstring"""
								A_								=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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




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







								"""simple docstring"""
								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=_snake_case							, initializer_range=self.initializer_range							, out_features=self.out_features							, out_indices=self.out_indices							, num_labels=self.num_labels							, )




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       List[str]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    ConvNextModel(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    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   lowerCamelCase__  (					self    :       int							, _snake_case    :       Tuple							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    ConvNextForImageClassification(_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case							, labels=_snake_case						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       str						)   ->			List[str]:







								"""simple docstring"""
								A_								=    ConvNextBackbone(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case						)

								# 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
								A_								=    None
								A_								=    ConvNextBackbone(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case						)

								# 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   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Tuple:







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





@require_torch
class __lowerCAmelCase					(       _lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(
			    (
			        ConvNextModel,
			        ConvNextForImageClassification,
			        ConvNextBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			snake_case						   =					(
			    {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case						   =					True
			snake_case						   =					False
			snake_case						   =					False
			snake_case						   =					False
			snake_case						   =					False




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Any:







								"""simple docstring"""
								A_								=    ConvNextModelTester(self						)
								A_								=    ConfigTester(self							, config_class=_snake_case							, has_text_modality=_snake_case							, hidden_size=37						)




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







								"""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    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								return




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







								"""simple docstring"""
								pass




			@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Tuple:







								"""simple docstring"""
								pass




			@unittest.skip(reason="ConvNext does not use feedforward chunking"						)
			def   lowerCamelCase__  (					self    :       Dict						)   ->			List[str]:







								"""simple docstring"""
								pass




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Any:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

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

													A_								=    ["pixel_values"]
													self.assertListEqual(arg_names[:1]							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			str:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_snake_case						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			str:







								"""simple docstring"""
								def check_hidden_states_output(_snake_case    :       Optional[Any]							, _snake_case    :       Optional[int]							, _snake_case    :       List[str]						):
													A_								=    model_class(_snake_case						)
													model.to(_snake_case						)
													model.eval()

													with torch.no_grad():
																		A_								=    model(**self._prepare_for_class(_snake_case							, _snake_case						)						)

													A_								=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A_								=    self.model_tester.num_stages
													self.assertEqual(len(_snake_case						)							, 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]							, )

								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_								=    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"]
													A_								=    True

													check_hidden_states_output(_snake_case							, _snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       int						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case						)






			@slow
			def   lowerCamelCase__  (					self    :       Any						)   ->			Tuple:







								"""simple docstring"""
								for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    ConvNextModel.from_pretrained(_snake_case						)
													self.assertIsNotNone(_snake_case						)





def 				A_  ():

					'''simple docstring'''





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



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




			"""simple docstring"""







			@cached_property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None






			@slow
			def   lowerCamelCase__  (					self    :       Dict						)   ->			Dict:







								"""simple docstring"""
								A_								=    ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						).to(_snake_case						)

								A_								=    self.default_image_processor
								A_								=    prepare_img()
								A_								=    image_processor(images=_snake_case							, return_tensors="pt"						).to(_snake_case						)

								# forward pass
								with torch.no_grad():
													A_								=    model(**_snake_case						)

								# verify the logits
								A_								=    torch.Size((1, 1_000)						)
								self.assertEqual(outputs.logits.shape							, _snake_case						)

								A_								=    torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]						).to(_snake_case						)

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





@require_torch
class __lowerCAmelCase					(       unittest.TestCase  ,					_lowercase   ):




			"""simple docstring"""







			snake_case						   =					(ConvNextBackbone,) if is_torch_available() else ()
			snake_case						   =					ConvNextConfig

			snake_case						   =					False




			def   lowerCamelCase__  (					self    :       str						)   ->			List[str]:







								"""simple docstring"""
								A_								=    ConvNextModelTester(self						)






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




