


"""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    lowerCamelCase__						:
      def __init__(		self	:	Union[str, Any]      ,       A_	:	List[Any]      ,       A_	:	Any=1_3      ,       A_	:	Optional[int]=3_0      ,       A_	:	Dict=2      ,       A_	:	List[Any]=3      ,       A_	:	List[str]=True      ,       A_	:	Any=True      ,       A_	:	List[Any]=3_2      ,       A_	:	Optional[int]=5      ,       A_	:	List[str]=4      ,       A_	:	List[Any]=3_7      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	Dict=0.1      ,       A_	:	Tuple=0.1      ,       A_	:	Any=1_0      ,       A_	:	List[str]=0.02      ,       A_	:	str=3      ,       A_	:	Dict=0.6      ,       A_	:	List[str]=None      ,       ):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	List[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=A_      ,       initializer_range=self.initializer_range      ,       mask_ratio=self.mask_ratio      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	Any							):






       '''simple docstring'''


       __lowercase									=	ViTMAEModel(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	Tuple      ,       A_	:	Union[str, Any]      ,       A_	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	ViTMAEForPreTraining(A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)
       __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(A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       __lowercase									=	model(A_							)
       __lowercase									=	self.patch_size**2
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, num_patches, expected_num_channels)							)






      def SCREAMING_SNAKE_CASE_	(		self	:	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    lowerCamelCase__						(		_a			,   _a			,   unittest.TestCase    ):
      a :     Optional[Any]											=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      a :     Any											=		{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

      a :     Any											=		False
      a :     Tuple											=		False
      a :     List[Any]											=		False
      a :     Optional[int]											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	ViTMAEModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=A_      ,       has_text_modality=A_      ,       hidden_size=3_7							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		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(A_							)
        self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)							)
        __lowercase									=	model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_      ,       nn.Linear							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(A_							)
        __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]      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Tuple      ,       A_	:	Dict      ,       A_	:	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(A_							)

       # 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(A_      ,       A_      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(A_							)
        model.to(A_							)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2							)
        with torch.no_grad():
         __lowercase									=	model(**self._prepare_for_class(A_      ,       A_							)							)

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

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

         # 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(A_      ,       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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


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






       '''simple docstring'''


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






       '''simple docstring'''


       pass






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


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






def lowerCAmelCase_      (				):






 """simple docstring"""







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





@require_torch
@require_vision
class    lowerCamelCase__						(		unittest.TestCase    ):
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


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






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       np.random.seed(2							)

       __lowercase									=	ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							).to(A_							)

       __lowercase									=	self.default_image_processor
       __lowercase									=	prepare_img()
       __lowercase									=	image_processor(images=A_      ,       return_tensors="""pt"""							).to(A_							)

       # 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(**A_      ,       noise=torch.from_numpy(A_							).to(device=A_							)							)

       # verify the logits
       __lowercase									=	torch.Size((1, 1_9_6, 7_6_8)							)
       self.assertEqual(outputs.logits.shape      ,       A_							)

       __lowercase									=	torch.tensor(
           [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]							)

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,       expected_slice.to(A_							)      ,       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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "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    lowerCamelCase__						(		_a    ):
      a :     Optional[int]											=		"""segformer"""
      def __init__(		self	:	str      ,       A_	:	Optional[int]=3      ,       A_	:	Any=4      ,       A_	:	Optional[int]=[2, 2, 2, 2]      ,       A_	:	str=[8, 4, 2, 1]      ,       A_	:	Any=[3_2, 6_4, 1_6_0, 2_5_6]      ,       A_	:	Union[str, Any]=[7, 3, 3, 3]      ,       A_	:	Any=[4, 2, 2, 2]      ,       A_	:	int=[1, 2, 5, 8]      ,       A_	:	Union[str, Any]=[4, 4, 4, 4]      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	List[str]=0.0      ,       A_	:	Any=0.0      ,       A_	:	Optional[Any]=0.1      ,       A_	:	List[str]=0.02      ,       A_	:	Tuple=0.1      ,       A_	:	Optional[Any]=1e-6      ,       A_	:	Optional[int]=2_5_6      ,       A_	:	Optional[Any]=2_5_5      ,       **A_	:	Union[str, Any]      ,       ):






       '''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_      ,       )

       __lowercase									=	num_channels
       __lowercase									=	num_encoder_blocks
       __lowercase									=	depths
       __lowercase									=	sr_ratios
       __lowercase									=	hidden_sizes
       __lowercase									=	patch_sizes
       __lowercase									=	strides
       __lowercase									=	mlp_ratios
       __lowercase									=	num_attention_heads
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	classifier_dropout_prob
       __lowercase									=	initializer_range
       __lowercase									=	drop_path_rate
       __lowercase									=	layer_norm_eps
       __lowercase									=	decoder_hidden_size
       __lowercase									=	kwargs.get("""reshape_last_stage"""      ,       A_							)
       __lowercase									=	semantic_loss_ignore_index






class    lowerCamelCase__						(		_a    ):
      a :     List[str]											=		version.parse("""1.11"""    )
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


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






       '''simple docstring'''


       return 1e-4






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return 1_2
