
'''simple docstring'''






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     __snake_case		:
					def __init__(     self,    A,    A=13,    A=30,    A=2,    A=3,    A=True,    A=True,    A=32,    A=5,    A=4,    A=37,    A="gelu",    A=0.1,    A=0.1,    A=10,    A=0.02,    A=3,    A=0.6,    A=None,    ):

									"""simple docstring"""


									lowerCamelCase					:			int									= parent
									lowerCamelCase					:			List[str]									= batch_size
									lowerCamelCase					:			Tuple									= image_size
									lowerCamelCase					:			List[str]									= patch_size
									lowerCamelCase					:			Optional[Any]									= num_channels
									lowerCamelCase					:			Optional[Any]									= is_training
									lowerCamelCase					:			Any									= use_labels
									lowerCamelCase					:			Union[str, Any]									= hidden_size
									lowerCamelCase					:			Tuple									= num_hidden_layers
									lowerCamelCase					:			Optional[int]									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= intermediate_size
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			int									= hidden_dropout_prob
									lowerCamelCase					:			Any									= attention_probs_dropout_prob
									lowerCamelCase					:			str									= type_sequence_label_size
									lowerCamelCase					:			Optional[int]									= initializer_range
									lowerCamelCase					:			List[Any]									= mask_ratio
									lowerCamelCase					:			Dict									= scope

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

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									lowerCamelCase					:			int									= None
									if self.use_labels:
													lowerCamelCase					:			Optional[int]									= ids_tensor([self.batch_size],    self.type_sequence_label_size      )

									lowerCamelCase					:			Tuple									= self.get_config()

									return config, pixel_values, labels
					def     UpperCAmelCase_							(     self      ):

									"""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     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= ViTMAEModel(config=UpperCAmelCase__      )
									model.to(UpperCAmelCase__      )
									model.eval()
									lowerCamelCase					:			Dict									= model(UpperCAmelCase__      )
									self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= ViTMAEForPreTraining(UpperCAmelCase__      )
									model.to(UpperCAmelCase__      )
									model.eval()
									lowerCamelCase					:			Any									= model(UpperCAmelCase__      )
									lowerCamelCase					:			str									= (self.image_size // self.patch_size) ** 2
									lowerCamelCase					:			List[Any]									= self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, num_patches, expected_num_channels)      )

									# test greyscale images
									lowerCamelCase					:			str									= 1
									lowerCamelCase					:			Any									= ViTMAEForPreTraining(UpperCAmelCase__      )
									model.to(UpperCAmelCase__      )
									model.eval()
									lowerCamelCase					:			Tuple									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									lowerCamelCase					:			List[str]									= model(UpperCAmelCase__      )
									lowerCamelCase					:			int									= self.patch_size**2
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, num_patches, expected_num_channels)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.prepare_config_and_inputs()
									lowerCamelCase					:			int									= config_and_inputs
									lowerCamelCase					:			List[Any]									= {'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_torch
class     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					_lowerCAmelCase			     =	{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= ViTMAEModelTester(self      )
									lowerCamelCase					:			Tuple									= ConfigTester(self,    config_class=UpperCAmelCase__,    has_text_modality=UpperCAmelCase__,    hidden_size=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					@unittest.skip(reason='ViTMAE does not use inputs_embeds'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			List[Any]									= model_class(UpperCAmelCase__      )
													self.assertIsInstance(model.get_input_embeddings(),    (nn.Module)      )
													lowerCamelCase					:			str									= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(UpperCAmelCase__,    nn.Linear      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			Tuple									= model_class(UpperCAmelCase__      )
													lowerCamelCase					:			int									= inspect.signature(model.forward      )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase					:			List[str]									= [*signature.parameters.keys()]

													lowerCamelCase					:			Any									= ['''pixel_values''']
													self.assertListEqual(arg_names[:1],    UpperCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									np.random.seed(2      )

									lowerCamelCase					:			Optional[Any]									= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2      )
									lowerCamelCase					:			Any									= np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
									lowerCamelCase					:			List[str]									= torch.from_numpy(UpperCAmelCase__      )

									# Add `noise` argument.
									# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
									lowerCamelCase					:			Optional[Any]									= pt_noise

									super().check_pt_tf_models(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			Optional[int]									= model_class(UpperCAmelCase__      )
													model.to(UpperCAmelCase__      )
													model.eval()
													# make random mask reproducible
													torch.manual_seed(2      )
													with torch.no_grad():
																	lowerCamelCase					:			List[Any]									= model(**self._prepare_for_class(UpperCAmelCase__,    UpperCAmelCase__      )      )

													lowerCamelCase					:			Optional[int]									= outputs[0].cpu().numpy()
													lowerCamelCase					:			List[Any]									= 0

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

																	# Make sure we don't have nans
																	lowerCamelCase					:			Union[str, Any]									= after_outputs[0].cpu().numpy()
																	lowerCamelCase					:			Tuple									= 0
																	lowerCamelCase					:			Optional[Any]									= 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\n    to get deterministic results.'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(
					    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(
					    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase					:			Dict									= ViTMAEModel.from_pretrained(UpperCAmelCase__      )
													self.assertIsNotNone(UpperCAmelCase__      )





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			List[Any]									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
				return image




@require_torch
@require_vision
class     __snake_case		(   unittest.TestCase):
					@cached_property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									np.random.seed(2      )

									lowerCamelCase					:			str									= ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'      ).to(UpperCAmelCase__      )

									lowerCamelCase					:			Any									= self.default_image_processor
									lowerCamelCase					:			List[Any]									= prepare_img()
									lowerCamelCase					:			int									= 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)
									lowerCamelCase					:			Dict									= ViTMAEConfig()
									lowerCamelCase					:			Union[str, Any]									= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2      )
									lowerCamelCase					:			Tuple									= np.random.uniform(size=(1, num_patches)      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			List[str]									= model(**UpperCAmelCase__,    noise=torch.from_numpy(UpperCAmelCase__      ).to(device=UpperCAmelCase__      )      )

									# verify the logits
									lowerCamelCase					:			Optional[Any]									= torch.Size((1, 196, 768)      )
									self.assertEqual(outputs.logits.shape,    UpperCAmelCase__      )

									lowerCamelCase					:			List[str]									= torch.tensor(
									    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]      )

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


'''simple docstring'''






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A			      =      logging.get_logger(__name__)

A			      =      {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''segformer'''
					def __init__(     self,    A=3,    A=4,    A=[2, 2, 2, 2],    A=[8, 4, 2, 1],    A=[32, 64, 160, 256],    A=[7, 3, 3, 3],    A=[4, 2, 2, 2],    A=[1, 2, 5, 8],    A=[4, 4, 4, 4],    A="gelu",    A=0.0,    A=0.0,    A=0.1,    A=0.02,    A=0.1,    A=1e-6,    A=256,    A=255,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
													warnings.warn(
													    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
													    ' removed, as the behaviour will default to that of reshape_last_stage = True.',    A,    )

									lowerCamelCase					:			Tuple									= num_channels
									lowerCamelCase					:			Dict									= num_encoder_blocks
									lowerCamelCase					:			Dict									= depths
									lowerCamelCase					:			Any									= sr_ratios
									lowerCamelCase					:			List[Any]									= hidden_sizes
									lowerCamelCase					:			List[Any]									= patch_sizes
									lowerCamelCase					:			Tuple									= strides
									lowerCamelCase					:			int									= mlp_ratios
									lowerCamelCase					:			Dict									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			Tuple									= hidden_dropout_prob
									lowerCamelCase					:			Optional[int]									= attention_probs_dropout_prob
									lowerCamelCase					:			List[str]									= classifier_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= initializer_range
									lowerCamelCase					:			Union[str, Any]									= drop_path_rate
									lowerCamelCase					:			int									= layer_norm_eps
									lowerCamelCase					:			Any									= decoder_hidden_size
									lowerCamelCase					:			Optional[int]									= kwargs.get('reshape_last_stage',    A      )
									lowerCamelCase					:			Any									= semantic_loss_ignore_index





class     __snake_case		(   a__):
					_lowerCAmelCase			     =	version.parse('''1.11''')
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 1e-4




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
