



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       Tuple				,					_snake_case					:       Optional[Any]=13				,					_snake_case					:       str=32				,					_snake_case					:       Tuple=3				,					_snake_case					:       int=4				,					_snake_case					:       Any=[10, 20, 30, 40]				,					_snake_case					:       Optional[int]=[2, 2, 3, 2]				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       List[str]=True				,					_snake_case					:       Any=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=10				,					_snake_case					:       Optional[int]=0.02				,					_snake_case					:       List[str]=["stage2", "stage3", "stage4"]				,					_snake_case					:       Union[str, Any]=[2, 3, 4]				,					_snake_case					:       List[str]=None				,					):






								"""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     _a							(     self					:       Tuple					):






								"""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     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								return ConvNextVaConfig(
								    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     _a							(     self					:       List[str]				,					_snake_case					:       List[str]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ConvNextVaModel(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     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Tuple				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    ConvNextVaForImageClassification(_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     _a							(     self					:       Optional[int]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    ConvNextVaBackbone(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__       =    ConvNextVaBackbone(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     _a							(     self					:       Tuple					):






								"""simple docstring"""


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



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


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







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Dict			       =    (
					    (
					        ConvNextVaModel,
					        ConvNextVaForImageClassification,
					        ConvNextVaBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					A__				:		List[str]			       =    (
					    {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__				:		Optional[int]			       =    False
					A__				:		Union[str, Any]			       =    False
					A__				:		Tuple			       =    False
					A__				:		Dict			       =    False
					A__				:		str			       =    False



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    ConvNextVaModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       List[str]					):






								"""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     _a							(     self					:       str					):






								"""simple docstring"""


								return



					@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'					)
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'					)
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'					)
					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       str					):






								"""simple docstring"""


								if not self.model_tester.is_training:
											return

								for model_class in self.all_model_classes:
											A__     , A__       =    self.model_tester.prepare_config_and_inputs_with_labels()
											A__       =    True

											if model_class.__name__ in [
											    *get_values(_snake_case					),
											    *get_values(_snake_case					),
											]:
														continue

											A__       =    model_class(_snake_case					)
											model.to(_snake_case					)
											model.train()
											A__       =    self._prepare_for_class(_snake_case				,					_snake_case				,					return_labels=_snake_case					)
											A__       =    model(**_snake_case					).loss
											loss.backward()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								if not self.model_tester.is_training:
											return

								for model_class in self.all_model_classes:
											A__     , A__       =    self.model_tester.prepare_config_and_inputs_with_labels()
											A__       =    False
											A__       =    True

											if (
											    model_class.__name__
											    in [*get_values(_snake_case					), *get_values(_snake_case					)]
											    or not model_class.supports_gradient_checkpointing
											):
														continue

											A__       =    model_class(_snake_case					)
											model.to(_snake_case					)
											model.gradient_checkpointing_enable()
											model.train()
											A__       =    self._prepare_for_class(_snake_case				,					_snake_case				,					return_labels=_snake_case					)
											A__       =    model(**_snake_case					).loss
											loss.backward()



					def     _a							(     self					:       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     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case					:       Tuple				,					_snake_case					:       List[Any]				,					_snake_case					:       Optional[Any]					):
											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					)

											# ConvNextV2'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     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ConvNextVaModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Any:
			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     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'					) if is_vision_available() else None



					@slow
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'					).to(_snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    preprocessor(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, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([0.9996, 0.1966, -0.4386]					).to(_snake_case					)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]				,					_snake_case				,					atol=1E-4					)					)









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								if truncation is not None:
											if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'					)
											A__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								if return_tensors:
											return model_outputs[0]
								if self.framework == "pt":
											return model_outputs[0].tolist()
								elif self.framework == "tf":
											return model_outputs[0].numpy().tolist()



					def __call__(     self					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




