



"""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				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=3_2				,       lowerCamelCase__=3				,       lowerCamelCase__=4				,       lowerCamelCase__=[1_0, 2_0, 3_0, 4_0]				,       lowerCamelCase__=[2, 2, 3, 2]				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=1_0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=["stage2", "stage3", "stage4"]				,       lowerCamelCase__=[2, 3, 4]				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				num_stages
									_lowerCamelCase        =				hidden_sizes
									_lowerCamelCase        =				depths
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				num_labels
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				out_features
									_lowerCamelCase        =				out_indices
									_lowerCamelCase        =				scope

					def 			snake_case__      (    self   ):
									_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 			snake_case__      (    self   ):
									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=_UpperCamelCase				,       initializer_range=self.initializer_range				,       out_features=self.out_features				,       out_indices=self.out_indices				,       num_labels=self.num_labels				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				ConvNextModel(config=_UpperCamelCase   )
									model.to(_UpperCamelCase   )
									model.eval()
									_lowerCamelCase        =				model(_UpperCamelCase   )
									# 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 // 3_2, self.image_size // 3_2)				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				ConvNextForImageClassification(_UpperCamelCase   )
									model.to(_UpperCamelCase   )
									model.eval()
									_lowerCamelCase        =				model(_UpperCamelCase				,       labels=_UpperCamelCase   )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				ConvNextBackbone(config=_UpperCamelCase   )
									model.to(_UpperCamelCase   )
									model.eval()
									_lowerCamelCase        =				model(_UpperCamelCase   )

									# 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        =				None
									_lowerCamelCase        =				ConvNextBackbone(config=_UpperCamelCase   )
									model.to(_UpperCamelCase   )
									model.eval()
									_lowerCamelCase        =				model(_UpperCamelCase   )

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


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





					'''simple docstring'''



					lowercase__   :       str										= (
					    (
					        ConvNextModel,
					        ConvNextForImageClassification,
					        ConvNextBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__   :       Dict										= (
					    {'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowercase__   :       int										= True
					lowercase__   :       str										= False
					lowercase__   :       str										= False
					lowercase__   :       Any										= False
					lowercase__   :       str										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ConvNextModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=_UpperCamelCase				,       has_text_modality=_UpperCamelCase				,       hidden_size=3_7   )

					def 			snake_case__      (    self   ):
									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 			snake_case__      (    self   ):
									return

					@unittest.skip(reason='''ConvNext does not use inputs_embeds'''   )
					def 			snake_case__      (    self   ):
									pass

					@unittest.skip(reason='''ConvNext does not support input and output embeddings'''   )
					def 			snake_case__      (    self   ):
									pass

					@unittest.skip(reason='''ConvNext does not use feedforward chunking'''   )
					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(_UpperCamelCase   )
													_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]				,       _UpperCamelCase   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*_UpperCamelCase   )

					def 			snake_case__      (    self   ):


									def check_hidden_states_output(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
													_lowerCamelCase        =				model_class(_UpperCamelCase   )
													model.to(_UpperCamelCase   )
													model.eval()

													with torch.no_grad():
																	_lowerCamelCase        =				model(**self._prepare_for_class(_UpperCamelCase				,       _UpperCamelCase   )   )

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

													_lowerCamelCase        =				self.model_tester.num_stages
													self.assertEqual(len(_UpperCamelCase   )				,       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        =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				True
													check_hidden_states_output(_UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase   )

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

													check_hidden_states_output(_UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase   )





					@slow
					def 			snake_case__      (    self   ):


									for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase        =				ConvNextModel.from_pretrained(_UpperCamelCase   )
													self.assertIsNotNone(_UpperCamelCase   )



def        lowerCAmelCase_(   )	->       List[str]:
				_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image


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





					'''simple docstring'''



					@cached_property
					def 			snake_case__      (    self   ):
									return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''   ) if is_vision_available() else None





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''   ).to(_UpperCamelCase   )

									_lowerCamelCase        =				self.default_image_processor
									_lowerCamelCase        =				prepare_img()
									_lowerCamelCase        =				image_processor(images=_UpperCamelCase				,       return_tensors='''pt'''   ).to(_UpperCamelCase   )

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

									# verify the logits
									_lowerCamelCase        =				torch.Size((1, 1_0_0_0)   )
									self.assertEqual(outputs.logits.shape				,       _UpperCamelCase   )

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

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


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





					'''simple docstring'''



					lowercase__   :       int										= (ConvNextBackbone,) if is_torch_available() else ()
					lowercase__   :       List[Any]										= ConvNextConfig

					lowercase__   :       Union[str, Any]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ConvNextModelTester(self   )





"""simple docstring"""
import warnings

from .generation import TFGenerationMixin


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					warnings.warn(
					    'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
					    'be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.',				A__,				)
