



'''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 _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Dict=1_3  ,       _lowerCAmelCase  :  Union[str, Any]=3_2  ,       _lowerCAmelCase  :  Union[str, Any]=3  ,       _lowerCAmelCase  :  Optional[Any]=4  ,       _lowerCAmelCase  :  Any=[1_0, 2_0, 3_0, 4_0]  ,       _lowerCAmelCase  :  List[Any]=[2, 2, 3, 2]  ,       _lowerCAmelCase  :  Optional[int]=True  ,       _lowerCAmelCase  :  str=True  ,       _lowerCAmelCase  :  List[Any]=3_7  ,       _lowerCAmelCase  :  Tuple="gelu"  ,       _lowerCAmelCase  :  Union[str, Any]=1_0  ,       _lowerCAmelCase  :  Tuple=0.02  ,       _lowerCAmelCase  :  str=["stage2", "stage3", "stage4"]  ,       _lowerCAmelCase  :  int=[2, 3, 4]  ,       _lowerCAmelCase  :  str=None  ,       ):







											'''simple docstring'''

											__lowercase       	=parent
											__lowercase       	=batch_size
											__lowercase       	=image_size
											__lowercase       	=num_channels
											__lowercase       	=num_stages
											__lowercase       	=hidden_sizes
											__lowercase       	=depths
											__lowercase       	=is_training
											__lowercase       	=use_labels
											__lowercase       	=intermediate_size
											__lowercase       	=hidden_act
											__lowercase       	=num_labels
											__lowercase       	=initializer_range
											__lowercase       	=out_features
											__lowercase       	=out_indices
											__lowercase       	=scope

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

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

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

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

				def 				__lowerCamelCase     (							self  :  Any):







											'''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=_lowerCAmelCase  ,       initializer_range=self.initializer_range  ,       out_features=self.out_features  ,       out_indices=self.out_indices  ,       num_labels=self.num_labels  ,       )

				def 				__lowerCamelCase     (							self  :  Optional[int]  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											__lowercase       	=ConvNextModel(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=model(_lowerCAmelCase)
											# 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 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  Optional[Any]  ,       _lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	=ConvNextForImageClassification(_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=model(_lowerCAmelCase  ,       labels=_lowerCAmelCase)
											self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.num_labels))

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  int):







											'''simple docstring'''

											__lowercase       	=ConvNextBackbone(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=model(_lowerCAmelCase)

											# 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
											__lowercase       	=None
											__lowercase       	=ConvNextBackbone(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=model(_lowerCAmelCase)

											# 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  :  List[str]):







											'''simple docstring'''

											__lowercase       	=self.prepare_config_and_inputs()
											__lowercase	,				__lowercase	,				__lowercase       	=config_and_inputs
											__lowercase       	={'pixel_values': pixel_values}
											return config, inputs_dict





@require_torch
class _UpperCamelCase		(     A      ,       A      ,       unittest.TestCase		):





				'''simple docstring'''





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

				lowerCAmelCase__					    =    True
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=ConvNextModelTester(self)
											__lowercase       	=ConfigTester(self  ,       config_class=_lowerCAmelCase  ,       has_text_modality=_lowerCAmelCase  ,       hidden_size=3_7)

				def 				__lowerCamelCase     (							self  :  Dict):







											'''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  :  Optional[int]):







											'''simple docstring'''

											return

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







											'''simple docstring'''

											pass

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







											'''simple docstring'''

											pass

				@unittest.skip(reason='ConvNext does not use feedforward chunking')
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											pass

				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											__lowercase	,				__lowercase       	=self.model_tester.prepare_config_and_inputs_for_common()

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

																		__lowercase       	=['pixel_values']
																		self.assertListEqual(arg_names[:1]  ,       _lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*_lowerCAmelCase)

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







											'''simple docstring'''

											def check_hidden_states_output(_lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  Optional[Any]  ,       _lowerCAmelCase  :  List[Any]):
																		__lowercase       	=model_class(_lowerCAmelCase)
																		model.to(_lowerCAmelCase)
																		model.eval()

																		with torch.no_grad():
																									__lowercase       	=model(**self._prepare_for_class(_lowerCAmelCase  ,       _lowerCAmelCase))

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

																		__lowercase       	=self.model_tester.num_stages
																		self.assertEqual(len(_lowerCAmelCase)  ,       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]  ,       )

											__lowercase	,				__lowercase       	=self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		__lowercase       	=True
																		check_hidden_states_output(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

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

																		check_hidden_states_output(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

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







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase)

				@slow
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

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



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image





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





				'''simple docstring'''





				@cached_property
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

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

				@slow
				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224').to(_lowerCAmelCase)

											__lowercase       	=self.default_image_processor
											__lowercase       	=prepare_img()
											__lowercase       	=image_processor(images=_lowerCAmelCase  ,       return_tensors='pt').to(_lowerCAmelCase)

											# forward pass
											with torch.no_grad():
																		__lowercase       	=model(**_lowerCAmelCase)

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

											__lowercase       	=torch.tensor([-0.0260, -0.4739, 0.1911]).to(_lowerCAmelCase)

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





@require_torch
class _UpperCamelCase		(     unittest.TestCase      ,       A		):





				'''simple docstring'''





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

				lowerCAmelCase__					    =    False

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=ConvNextModelTester(self)








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase	 =       {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_bertweet import BertweetTokenizer

else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



