


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

    if is_vision_available():
        from transformers import MaskaFormerImageProcessor

if is_vision_available():
    from PIL import Image



class A__     :



  """simple docstring"""



  def __init__( self			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  str=2				,				lowerCamelCase__			:  Dict=True				,				lowerCamelCase__			:  List[str]=False				,				lowerCamelCase__			:  Tuple=10				,				lowerCamelCase__			:  int=3				,				lowerCamelCase__			:  Tuple=32 * 8				,				lowerCamelCase__			:  Union[str, Any]=32 * 8				,				lowerCamelCase__			:  List[Any]=4				,				lowerCamelCase__			:  Optional[int]=64				,				):
        a__	:  List[str]     =   parent
        a__	:  List[Any]     =   batch_size
        a__	:  Any     =   is_training
        a__	:  Optional[Any]     =   use_auxiliary_loss
        a__	:  Dict     =   num_queries
        a__	:  Any     =   num_channels
        a__	:  List[Any]     =   min_size
        a__	:  Dict     =   max_size
        a__	:  Dict     =   num_labels
        a__	:  str     =   hidden_dim
        a__	:  int     =   hidden_dim

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  List[Any]     =   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
            lowerCamelCase__       )

        a__	:  List[str]     =   torch.ones([self.batch_size, self.min_size, self.max_size]				,				device=lowerCamelCase__       )

        a__	:  List[str]     =   (
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]				,				device=lowerCamelCase__       ) > 0.5
        ).float()
        a__	:  Optional[int]     =   (torch.rand((self.batch_size, self.num_labels)				,				device=lowerCamelCase__       ) > 0.5).long()

        a__	:  Tuple     =   self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  Dict     =   MaskaFormerConfig(
            hidden_size=self.hidden_dim				,				)
        a__	:  str     =   self.num_queries
        a__	:  Tuple     =   self.num_labels

        a__	:  Optional[int]     =   [1, 1, 1, 1]
        a__	:  Dict     =   self.num_channels

        a__	:  int     =   64
        a__	:  Optional[Any]     =   128
        a__	:  Optional[int]     =   self.hidden_dim
        a__	:  Dict     =   self.hidden_dim
        a__	:  Optional[Any]     =   self.hidden_dim
        return config

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__,	a__,	a__,	a__,	a__	:  Union[str, Any]     =   self.prepare_config_and_inputs()
        a__	:  Dict     =   {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
        return config, inputs_dict

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  List[Any]       ):
        a__	:  Any     =   output.encoder_hidden_states
        a__	:  Optional[Any]     =   output.pixel_decoder_hidden_states
        a__	:  str     =   output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(lowerCamelCase__       )				,				len(config.backbone_config.depths       )       )
        self.parent.assertTrue(len(lowerCamelCase__       )				,				len(config.backbone_config.depths       )       )
        self.parent.assertTrue(len(lowerCamelCase__       )				,				config.decoder_layers       )

  def    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Dict				,				lowerCamelCase__			:  Optional[int]=False       ):
        with torch.no_grad():
              a__	:  List[str]     =   MaskaFormerModel(config=lowerCamelCase__       )
              model.to(lowerCamelCase__       )
              model.eval()

              a__	:  int     =   model(pixel_values=lowerCamelCase__				,				pixel_mask=lowerCamelCase__       )
              a__	:  List[str]     =   model(lowerCamelCase__				,				output_hidden_states=lowerCamelCase__       )

        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape				,				(self.batch_size, self.num_queries, self.hidden_dim)				,				)
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
        self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

        if output_hidden_states:
              self.check_output_hidden_state(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  Dict       ):
        a__	:  str     =   MaskaFormerForUniversalSegmentation(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()

        def comm_check_on_output(lowerCamelCase__			:  Optional[Any]       ):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
              self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape				,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)				,				)
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape				,				(self.batch_size, self.num_queries, self.num_labels + 1)       )

        with torch.no_grad():
              a__	:  Tuple     =   model(pixel_values=lowerCamelCase__				,				pixel_mask=lowerCamelCase__       )
              a__	:  int     =   model(lowerCamelCase__       )

              comm_check_on_output(lowerCamelCase__       )

              a__	:  Optional[Any]     =   model(
                  pixel_values=lowerCamelCase__				,				pixel_mask=lowerCamelCase__				,				mask_labels=lowerCamelCase__				,				class_labels=lowerCamelCase__       )

        comm_check_on_output(lowerCamelCase__       )

        self.parent.assertTrue(result.loss is not None       )
        self.parent.assertEqual(result.loss.shape				,				torch.Size([1]       )       )




@require_torch
class A__     ( A__				,		A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  _lowercase									=		{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

  _lowercase									=		False
  _lowercase									=		False
  _lowercase									=		False
  _lowercase									=		False

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Dict     =   MaskaFormerModelTester(self       )
        a__	:  List[str]     =   ConfigTester(self				,				config_class=lowerCamelCase__				,				has_text_modality=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        self.config_tester.run_common_tests()

  def    _UpperCamelCase( self			:  int       ):
        a__,	a__	:  Dict     =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__				,				**lowerCamelCase__				,				output_hidden_states=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  List[str]     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__       )

  @unittest.skip(reason="Mask2Former does not use inputs_embeds"       )
  def    _UpperCamelCase( self			:  str       ):
        pass

  @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"       )
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        pass

  @unittest.skip(reason="Mask2Former is not a generative model"       )
  def    _UpperCamelCase( self			:  Dict       ):
        pass

  @unittest.skip(reason="Mask2Former does not use token embeddings"       )
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        pass

  @require_torch_multi_gpu
  @unittest.skip(
      reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"       )
  def    _UpperCamelCase( self			:  Tuple       ):
        pass

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

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__,	a__	:  List[Any]     =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              a__	:  Dict     =   model_class(lowerCamelCase__       )
              a__	:  Optional[Any]     =   inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              a__	:  List[str]     =   [*signature.parameters.keys()]

              a__	:  Optional[Any]     =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]				,				lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  Any       ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              a__	:  Union[str, Any]     =   MaskaFormerModel.from_pretrained(lowerCamelCase__       )
              self.assertIsNotNone(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int       ):
        a__	:  int     =   (self.model_tester.min_size,) * 2
        a__	:  Tuple     =   {
            "pixel_values": torch.randn((2, 3, *size)				,				device=lowerCamelCase__       ),
            "mask_labels": torch.randn((2, 10, *size)				,				device=lowerCamelCase__       ),
            "class_labels": torch.zeros(2				,				10				,				device=lowerCamelCase__       ).long(),
        }
        a__	:  int     =   self.model_tester.get_config()

        a__	:  Any     =   MaskaFormerForUniversalSegmentation(lowerCamelCase__       ).to(lowerCamelCase__       )
        a__	:  Optional[int]     =   model(**lowerCamelCase__       )
        self.assertTrue(outputs.loss is not None       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__,	a__	:  Dict     =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__				,				**lowerCamelCase__				,				output_hidden_states=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__,	a__	:  Optional[int]     =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              a__	:  Optional[Any]     =   model_class(lowerCamelCase__       ).to(lowerCamelCase__       )
              a__	:  Any     =   model(**lowerCamelCase__				,				output_attentions=lowerCamelCase__       )
              self.assertTrue(outputs.attentions is not None       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        if not self.model_tester.is_training:
              return

        a__	:  Union[str, Any]     =   self.all_model_classes[1]
        a__,	a__,	a__,	a__,	a__	:  List[str]     =   self.model_tester.prepare_config_and_inputs()

        a__	:  Tuple     =   model_class(lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.train()

        a__	:  int     =   model(lowerCamelCase__				,				mask_labels=lowerCamelCase__				,				class_labels=lowerCamelCase__       ).loss
        loss.backward()

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  str     =   self.all_model_classes[1]
        a__,	a__,	a__,	a__,	a__	:  int     =   self.model_tester.prepare_config_and_inputs()
        a__	:  Optional[Any]     =   True
        a__	:  str     =   True

        a__	:  Any     =   model_class(lowerCamelCase__       ).to(lowerCamelCase__       )
        model.train()

        a__	:  Union[str, Any]     =   model(lowerCamelCase__				,				mask_labels=lowerCamelCase__				,				class_labels=lowerCamelCase__       )

        a__	:  Dict     =   outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        a__	:  Optional[Any]     =   outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        a__	:  List[Any]     =   outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        a__	:  List[str]     =   outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=lowerCamelCase__       )

        self.assertIsNotNone(encoder_hidden_states.grad       )
        self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
        self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
        self.assertIsNotNone(attentions.grad       )




UpperCamelCase					:       Any			    =     1E-4



def UpperCamelCase_				(				)	->   str:
      a__	:  str     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
      return image



@require_vision
@slow
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  @cached_property
  def    _UpperCamelCase( self			:  str       ):
        return "facebook/mask2former-swin-small-coco-instance"

  @cached_property
  def    _UpperCamelCase( self			:  str       ):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Dict     =   MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(lowerCamelCase__       )
        a__	:  List[Any]     =   self.default_image_processor
        a__	:  List[Any]     =   prepare_img()
        a__	:  Optional[Any]     =   image_processor(lowerCamelCase__				,				return_tensors="pt"       ).to(lowerCamelCase__       )
        a__	:  Dict     =   inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
        # check size
        self.assertEqual(lowerCamelCase__				,				(1, 3, 384, 384)       )

        with torch.no_grad():
              a__	:  str     =   model(**lowerCamelCase__       )

        a__	:  str     =   torch.tensor(
            [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]       ).to(lowerCamelCase__       )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]				,				lowerCamelCase__				,				atol=lowerCamelCase__       )       )

        a__	:  List[str]     =   torch.tensor(
            [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]       ).to(lowerCamelCase__       )
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,				lowerCamelCase__				,				atol=lowerCamelCase__       )       )

        a__	:  Tuple     =   torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]       ).to(lowerCamelCase__       )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,				lowerCamelCase__				,				atol=lowerCamelCase__       )       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  str     =   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(lowerCamelCase__       ).eval()
        a__	:  List[Any]     =   self.default_image_processor
        a__	:  str     =   prepare_img()
        a__	:  Union[str, Any]     =   image_processor(lowerCamelCase__				,				return_tensors="pt"       ).to(lowerCamelCase__       )
        a__	:  str     =   inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
        # check size
        self.assertEqual(lowerCamelCase__				,				(1, 3, 384, 384)       )

        with torch.no_grad():
              a__	:  Union[str, Any]     =   model(**lowerCamelCase__       )
        # masks_queries_logits
        a__	:  Tuple     =   outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape				,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
        a__	:  Optional[int]     =   [
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        a__	:  int     =   torch.tensor(lowerCamelCase__       ).to(lowerCamelCase__       )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,				lowerCamelCase__				,				atol=lowerCamelCase__       )       )
        # class_queries_logits
        a__	:  Any     =   outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape				,				(1, model.config.num_queries, model.config.num_labels + 1)       )
        a__	:  List[str]     =   torch.tensor(
            [
                [1.8324, -8.0835, -4.1922],
                [0.8450, -9.0050, -3.6053],
                [0.3045, -7.7293, -3.0275],
            ]       ).to(lowerCamelCase__       )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,				lowerCamelCase__				,				atol=lowerCamelCase__       )       )

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Optional[Any]     =   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(lowerCamelCase__       ).eval()
        a__	:  List[Any]     =   self.default_image_processor

        a__	:  Union[str, Any]     =   image_processor(
            [np.zeros((3, 800, 1_333)       ), np.zeros((3, 800, 1_333)       )]				,				segmentation_maps=[np.zeros((384, 384)       ).astype(np.floataa       ), np.zeros((384, 384)       ).astype(np.floataa       )]				,				return_tensors="pt"				,				)

        a__	:  Any     =   inputs["pixel_values"].to(lowerCamelCase__       )
        a__	:  List[Any]     =   [el.to(lowerCamelCase__       ) for el in inputs["mask_labels"]]
        a__	:  List[Any]     =   [el.to(lowerCamelCase__       ) for el in inputs["class_labels"]]

        with torch.no_grad():
              a__	:  List[Any]     =   model(**lowerCamelCase__       )

        self.assertTrue(outputs.loss is not None       )







'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
