


import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

	from transformers import ViTMAEForPreTraining, ViTMAEModel
	from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Dict=13				, _UpperCAmelCase   :						Tuple=30				, _UpperCAmelCase   :						List[Any]=2				, _UpperCAmelCase   :						List[Any]=3				, _UpperCAmelCase   :						Optional[int]=True				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Tuple=32				, _UpperCAmelCase   :						str=5				, _UpperCAmelCase   :						List[Any]=4				, _UpperCAmelCase   :						Tuple=37				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						str=0.1				, _UpperCAmelCase   :						Union[str, Any]=0.1				, _UpperCAmelCase   :						Tuple=10				, _UpperCAmelCase   :						Tuple=0.02				, _UpperCAmelCase   :						str=3				, _UpperCAmelCase   :						Optional[int]=0.6				, _UpperCAmelCase   :						Optional[Any]=None				, )						->       int:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		patch_size
							lowercase__					      =		num_channels
							lowercase__					      =		is_training
							lowercase__					      =		use_labels
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_act
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		type_sequence_label_size
							lowercase__					      =		initializer_range
							lowercase__					      =		mask_ratio
							lowercase__					      =		scope

							# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
							# (we add 1 for the [CLS] token)
							lowercase__					      =		(image_size // patch_size) ** 2
							lowercase__					      =		int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       List[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.type_sequence_label_size		)

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

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




							"""simple docstring"""


							return ViTMAEConfig(
							    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=_UpperCAmelCase				, initializer_range=self.initializer_range				, mask_ratio=self.mask_ratio				, )

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




							"""simple docstring"""


							lowercase__					      =		ViTMAEModel(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)		)

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




							"""simple docstring"""


							lowercase__					      =		ViTMAEForPreTraining(_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)
							lowercase__					      =		(self.image_size // self.patch_size) ** 2
							lowercase__					      =		self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, num_patches, expected_num_channels)		)

							# test greyscale images
							lowercase__					      =		1
							lowercase__					      =		ViTMAEForPreTraining(_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							lowercase__					      =		model(_UpperCAmelCase		)
							lowercase__					      =		self.patch_size**2
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, num_patches, expected_num_channels)		)


					def   lowerCamelCase__					(self   :						str		)						->       Any:




							"""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 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					A__      =			{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False

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




							"""simple docstring"""


							lowercase__					      =		ViTMAEModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, has_text_modality=_UpperCAmelCase				, hidden_size=37		)

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




							"""simple docstring"""


							self.config_tester.run_common_tests()

					@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						Any		)						->       Optional[int]:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[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(_UpperCAmelCase		)
									self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)		)
									lowercase__					      =		model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_UpperCAmelCase				, nn.Linear		)		)

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




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__					      =		model_class(_UpperCAmelCase		)
									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]				, _UpperCAmelCase		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase		)

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




							"""simple docstring"""


							np.random.seed(2		)

							lowercase__					      =		int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
							lowercase__					      =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
							lowercase__					      =		torch.from_numpy(_UpperCAmelCase		)

							# Add `noise` argument.
							# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
							lowercase__					      =		pt_noise

							super().check_pt_tf_models(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       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(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									# make random mask reproducible
									torch.manual_seed(2		)
									with torch.no_grad():
											lowercase__					      =		model(**self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase		)		)

									lowercase__					      =		outputs[0].cpu().numpy()
									lowercase__					      =		0

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCAmelCase		)
											lowercase__					      =		model_class.from_pretrained(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											# make random mask reproducible
											torch.manual_seed(2		)
											with torch.no_grad():
													lowercase__					      =		model(**self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase		)		)

											# Make sure we don't have nans
											lowercase__					      =		after_outputs[0].cpu().numpy()
											lowercase__					      =		0
											lowercase__					      =		np.amax(np.abs(out_a - out_a		)		)
											self.assertLessEqual(_UpperCAmelCase				, 1E-5		)

					@unittest.skip(
					    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							pass

					@unittest.skip(
					    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
					def   lowerCamelCase__					(self   :						Dict		)						->       Dict:




							"""simple docstring"""


							pass

					@unittest.skip(
					    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[int]:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""		)
					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							pass

					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


							pass


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




							"""simple docstring"""


							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__					      =		ViTMAEModel.from_pretrained(_UpperCAmelCase		)
									self.assertIsNotNone(_UpperCAmelCase		)






def 			UpperCamelCase (     )       ->  Any:
		"""simple docstring"""




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







@require_torch
@require_vision
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@cached_property
					def   lowerCamelCase__					(self   :						str		)						->       Tuple:




							"""simple docstring"""


							return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""		) if is_vision_available() else None


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




							"""simple docstring"""


							np.random.seed(2		)

							lowercase__					      =		ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(_UpperCAmelCase		)

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(images=_UpperCAmelCase				, return_tensors="""pt"""		).to(_UpperCAmelCase		)

							# prepare a noise vector that will be also used for testing the TF model
							# (this way we can ensure that the PT and TF models operate on the same inputs)
							lowercase__					      =		ViTMAEConfig()
							lowercase__					      =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
							lowercase__					      =		np.random.uniform(size=(1, num_patches)		)

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**_UpperCAmelCase				, noise=torch.from_numpy(_UpperCAmelCase		).to(device=_UpperCAmelCase		)		)

							# verify the logits
							lowercase__					      =		torch.Size((1, 196, 768)		)
							self.assertEqual(outputs.logits.shape				, _UpperCAmelCase		)

							lowercase__					      =		torch.tensor(
							    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				, expected_slice.to(_UpperCAmelCase		)				, atol=1E-4		)		)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A	:		Optional[Any]    =       logging.get_logger(__name__)

A	:		Tuple    =       {
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''convbert'''

					def __init__(self   :						str				, _UpperCAmelCase   :						Union[str, Any]=3_0522				, _UpperCAmelCase   :						Dict=768				, _UpperCAmelCase   :						Optional[int]=12				, _UpperCAmelCase   :						List[str]=12				, _UpperCAmelCase   :						str=3072				, _UpperCAmelCase   :						Tuple="gelu"				, _UpperCAmelCase   :						Optional[int]=0.1				, _UpperCAmelCase   :						int=0.1				, _UpperCAmelCase   :						str=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						int=0.02				, _UpperCAmelCase   :						Tuple=1E-1_2				, _UpperCAmelCase   :						Dict=1				, _UpperCAmelCase   :						Optional[Any]=0				, _UpperCAmelCase   :						List[str]=2				, _UpperCAmelCase   :						str=768				, _UpperCAmelCase   :						List[Any]=2				, _UpperCAmelCase   :						Optional[Any]=9				, _UpperCAmelCase   :						List[Any]=1				, _UpperCAmelCase   :						Union[str, Any]=None				, **_UpperCAmelCase   :						List[Any]				, )						->       List[str]:




							"""simple docstring"""


							super().__init__(
							    pad_token_id=_UpperCAmelCase				, bos_token_id=_UpperCAmelCase				, eos_token_id=_UpperCAmelCase				, **_UpperCAmelCase				, )

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_act
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		embedding_size
							lowercase__					      =		head_ratio
							lowercase__					      =		conv_kernel_size
							lowercase__					      =		num_groups
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							        ("""token_type_ids""", dynamic_axis),
							    ]		)

