






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					:			Dict		,      __lowercase					:			Dict		,      __lowercase					:			Tuple=13		,      __lowercase					:			Any=30		,      __lowercase					:			Tuple=2		,      __lowercase					:			int=3		,      __lowercase					:			Optional[Any]=True		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Optional[Any]=32		,      __lowercase					:			List[Any]=5		,      __lowercase					:			Optional[int]=4		,      __lowercase					:			Tuple=37		,      __lowercase					:			List[str]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			List[Any]=0.1		,      __lowercase					:			Optional[int]=10		,      __lowercase					:			List[Any]=0.02		,      __lowercase					:			List[Any]=3		,      __lowercase					:			Union[str, Any]=0.6		,      __lowercase					:			Dict=None		,      )							->      Any:
        __UpperCAmelCase							:	List[Any]           =  parent
        __UpperCAmelCase							:	Optional[int]           =  batch_size
        __UpperCAmelCase							:	Any           =  image_size
        __UpperCAmelCase							:	Tuple           =  patch_size
        __UpperCAmelCase							:	Tuple           =  num_channels
        __UpperCAmelCase							:	List[Any]           =  is_training
        __UpperCAmelCase							:	Optional[Any]           =  use_labels
        __UpperCAmelCase							:	Optional[Any]           =  hidden_size
        __UpperCAmelCase							:	Any           =  num_hidden_layers
        __UpperCAmelCase							:	int           =  num_attention_heads
        __UpperCAmelCase							:	List[str]           =  intermediate_size
        __UpperCAmelCase							:	Union[str, Any]           =  hidden_act
        __UpperCAmelCase							:	Optional[int]           =  hidden_dropout_prob
        __UpperCAmelCase							:	Any           =  attention_probs_dropout_prob
        __UpperCAmelCase							:	List[Any]           =  type_sequence_label_size
        __UpperCAmelCase							:	Dict           =  initializer_range
        __UpperCAmelCase							:	str           =  mask_ratio
        __UpperCAmelCase							:	Optional[Any]           =  scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        __UpperCAmelCase							:	int           =  (image_size // patch_size) ** 2
        __UpperCAmelCase							:	Optional[Any]           =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)					)					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      List[str]:
        __UpperCAmelCase							:	Dict           =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        __UpperCAmelCase							:	int           =  self.get_config()

        return config, pixel_values, labels
      def 	UpperCAmelCase      (      self					:			int					)							->      Union[str, Any]:
        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=__lowercase		,      initializer_range=self.initializer_range		,      mask_ratio=self.mask_ratio		,      )
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Tuple		,      __lowercase					:			Dict		,      __lowercase					:			Optional[Any]					)							->      List[Any]:
        __UpperCAmelCase							:	Optional[int]           =  ViTMAEModel(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Optional[Any]           =  model(__lowercase					)
        self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Optional[Any]		,      __lowercase					:			List[Any]		,      __lowercase					:			Dict					)							->      Optional[int]:
        __UpperCAmelCase							:	Dict           =  ViTMAEForPreTraining(__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Any           =  model(__lowercase					)
        __UpperCAmelCase							:	List[str]           =  (self.image_size // self.patch_size) ** 2
        __UpperCAmelCase							:	Optional[int]           =  self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, num_patches, expected_num_channels)					)

        # test greyscale images
        __UpperCAmelCase							:	Union[str, Any]           =  1
        __UpperCAmelCase							:	Optional[Any]           =  ViTMAEForPreTraining(__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Dict           =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
        __UpperCAmelCase							:	Optional[Any]           =  model(__lowercase					)
        __UpperCAmelCase							:	Tuple           =  self.patch_size**2
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, num_patches, expected_num_channels)					)




      def 	UpperCAmelCase      (      self					:			Tuple					)							->      int:
        __UpperCAmelCase							:	Dict           =  self.prepare_config_and_inputs()
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	str           =  config_and_inputs
        __UpperCAmelCase							:	int           =  {"""pixel_values""": pixel_values}
        return config, inputs_dict


@require_torch
class     a    ( lowercase__							,       lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Optional[int]							       =	(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      a  : str							       =	{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

      a  : str							       =	False
      a  : Any							       =	False
      a  : Any							       =	False
      a  : int							       =	False
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Tuple:
        __UpperCAmelCase							:	List[Any]           =  ViTMAEModelTester(self					)
        __UpperCAmelCase							:	Dict           =  ConfigTester(self		,      config_class=__lowercase		,      has_text_modality=__lowercase		,      hidden_size=37					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        self.config_tester.run_common_tests()
      @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      int:
        pass
      def 	UpperCAmelCase      (      self					:			Any					)							->      str:
        __UpperCAmelCase      , __UpperCAmelCase							:	int           =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase							:	Tuple           =  model_class(__lowercase					)
          self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module)					)
          __UpperCAmelCase							:	Optional[Any]           =  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowercase		,      nn.Linear					)					)
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Any:
        __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  self.model_tester.prepare_config_and_inputs_for_common()

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

          __UpperCAmelCase							:	Tuple           =  ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Tuple:
        __UpperCAmelCase							:	int           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      List[Any]:
        __UpperCAmelCase							:	int           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			Tuple		,      __lowercase					:			str		,      __lowercase					:			Any					)							->      List[str]:
        # make masks reproducible
        np.random.seed(2					)

        __UpperCAmelCase							:	Tuple           =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2					)
        __UpperCAmelCase							:	Tuple           =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
        __UpperCAmelCase							:	Optional[int]           =  torch.from_numpy(__lowercase					)

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

        super().check_pt_tf_models(__lowercase		,      __lowercase		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      List[str]:
        __UpperCAmelCase      , __UpperCAmelCase							:	Dict           =  self.model_tester.prepare_config_and_inputs_for_common()

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

          __UpperCAmelCase							:	Any           =  outputs[0].cpu().numpy()
          __UpperCAmelCase							:	Union[str, Any]           =  0

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

            # Make sure we don't have nans
            __UpperCAmelCase							:	Any           =  after_outputs[0].cpu().numpy()
            __UpperCAmelCase							:	Union[str, Any]           =  0
            __UpperCAmelCase							:	str           =  np.amax(np.abs(out_a - out_a					)					)
            self.assertLessEqual(__lowercase		,      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 	UpperCAmelCase      (      self					:			str					)							->      Union[str, Any]:
        pass
      @unittest.skip(
          reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Tuple:
        pass
      @unittest.skip(
          reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
      def 	UpperCAmelCase      (      self					:			Any					)							->      Union[str, Any]:
        pass
      @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""					)
      def 	UpperCAmelCase      (      self					:			str					)							->      Any:
        pass
      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Any:
        pass




      @slow
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Dict:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase							:	Tuple           =  ViTMAEModel.from_pretrained(__lowercase					)
          self.assertIsNotNone(__lowercase					)






def 				lowerCamelCase__      (    ):
  __UpperCAmelCase							:	Any           =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
  return image







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



      """simple docstring"""


      @cached_property
      def 	UpperCAmelCase      (      self					:			Dict					)							->      int:
        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""					) if is_vision_available() else None




      @slow
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[Any]:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2					)

        __UpperCAmelCase							:	Any           =  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					).to(__lowercase					)

        __UpperCAmelCase							:	List[Any]           =  self.default_image_processor
        __UpperCAmelCase							:	Optional[Any]           =  prepare_img()
        __UpperCAmelCase							:	int           =  image_processor(images=__lowercase		,      return_tensors="""pt"""					).to(__lowercase					)

        # 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)
        __UpperCAmelCase							:	Dict           =  ViTMAEConfig()
        __UpperCAmelCase							:	str           =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
        __UpperCAmelCase							:	Dict           =  np.random.uniform(size=(1, num_patches)					)

        # forward pass
        with torch.no_grad():
          __UpperCAmelCase							:	Optional[Any]           =  model(**__lowercase		,      noise=torch.from_numpy(__lowercase					).to(device=__lowercase					)					)

        # verify the logits
        __UpperCAmelCase							:	Dict           =  torch.Size((1, 196, 768)					)
        self.assertEqual(outputs.logits.shape		,      __lowercase					)

        __UpperCAmelCase							:	Tuple           =  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(__lowercase					)		,      atol=1e-4					)					)










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		:       List[Any]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "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     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	'segformer'
      def __init__(      self					:			Dict		,      __lowercase					:			str=3		,      __lowercase					:			Dict=4		,      __lowercase					:			Any=[2, 2, 2, 2]		,      __lowercase					:			Optional[int]=[8, 4, 2, 1]		,      __lowercase					:			List[str]=[32, 64, 160, 256]		,      __lowercase					:			Union[str, Any]=[7, 3, 3, 3]		,      __lowercase					:			Optional[int]=[4, 2, 2, 2]		,      __lowercase					:			Any=[1, 2, 5, 8]		,      __lowercase					:			List[str]=[4, 4, 4, 4]		,      __lowercase					:			Any="gelu"		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.0		,      __lowercase					:			Optional[Any]=0.1		,      __lowercase					:			int=0.02		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			Optional[Any]=1e-6		,      __lowercase					:			Tuple=256		,      __lowercase					:			List[Any]=255		,      **__lowercase					:			Union[str, Any]		,      )							->      List[Any]:
        super().__init__(**__lowercase					)

        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."""		,      __lowercase		,      )

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


class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Union[str, Any]							       =	version.parse('1.11'   )
      @property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]					)
      @property
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      float:
        return 1e-4




      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      int:
        return 12


