"""simple docstring"""






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     UpperCAmelCase						:



    """simple docstring"""

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






    def _snake_case			(   self							):
       lowercase__:   Tuple					=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
           _UpperCAmelCase							)

       lowercase__:   Tuple					=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,			device=_UpperCAmelCase							)

       lowercase__:   Dict					=      (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,			device=_UpperCAmelCase							) > 0.5
       ).float()
       lowercase__:   Optional[int]					=      (torch.rand((self.batch_size, self.num_labels)    ,			device=_UpperCAmelCase							) > 0.5).long()

       lowercase__:   Optional[Any]					=      self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels






    def _snake_case			(   self							):
       lowercase__:   Dict					=      MaskaFormerConfig(
           hidden_size=self.hidden_dim    ,			)
       lowercase__:   Union[str, Any]					=      self.num_queries
       lowercase__:   Optional[Any]					=      self.num_labels

       lowercase__:   Any					=      [1, 1, 1, 1]
       lowercase__:   Tuple					=      self.num_channels

       lowercase__:   Any					=      64
       lowercase__:   int					=      128
       lowercase__:   Any					=      self.hidden_dim
       lowercase__:   Union[str, Any]					=      self.hidden_dim
       lowercase__:   Dict					=      self.hidden_dim
       return config






    def _snake_case			(   self							):
       lowercase__, lowercase__, lowercase__, lowercase__, lowercase__:   int					=      self.prepare_config_and_inputs()
       lowercase__:   List[Any]					=      {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
       return config, inputs_dict






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      output.encoder_hidden_states
       lowercase__:   Tuple					=      output.pixel_decoder_hidden_states
       lowercase__:   Optional[int]					=      output.transformer_decoder_hidden_states

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






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False							):
       with torch.no_grad():
          lowercase__:   str					=      MaskaFormerModel(config=_UpperCAmelCase							)
          model.to(_UpperCAmelCase							)
          model.eval()

          lowercase__:   Optional[int]					=      model(pixel_values=_UpperCAmelCase    ,			pixel_mask=_UpperCAmelCase							)
          lowercase__:   int					=      model(_UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase							)

       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(_UpperCAmelCase    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      MaskaFormerForUniversalSegmentation(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()

       def comm_check_on_output(_UpperCAmelCase							):
          # 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():
          lowercase__:   List[Any]					=      model(pixel_values=_UpperCAmelCase    ,			pixel_mask=_UpperCAmelCase							)
          lowercase__:   int					=      model(_UpperCAmelCase							)

          comm_check_on_output(_UpperCAmelCase							)

          lowercase__:   Any					=      model(
              pixel_values=_UpperCAmelCase    ,			pixel_mask=_UpperCAmelCase    ,			mask_labels=_UpperCAmelCase    ,			class_labels=_UpperCAmelCase							)

       comm_check_on_output(_UpperCAmelCase							)

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



@require_torch
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    _UpperCAmelCase	:Union[str, Any]						    = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

    _UpperCAmelCase	:Any						    = False
    _UpperCAmelCase	:Union[str, Any]						    = False
    _UpperCAmelCase	:str						    = False
    _UpperCAmelCase	:List[Any]						    = False






    def _snake_case			(   self							):
       lowercase__:   int					=      MaskaFormerModelTester(self							)
       lowercase__:   Optional[int]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase    ,			has_text_modality=_UpperCAmelCase							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__, lowercase__:   Optional[Any]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase    ,			**_UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   str					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_UpperCAmelCase							)






    @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''							)
    def _snake_case			(   self							):
       pass






    @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''							)
    def _snake_case			(   self							):
       pass






    @unittest.skip(reason='''Mask2Former is not a generative model'''							)
    def _snake_case			(   self							):
       pass






    @unittest.skip(reason='''Mask2Former does not use token embeddings'''							)
    def _snake_case			(   self							):
       pass






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






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






    def _snake_case			(   self							):
       lowercase__, lowercase__:   List[Any]					=      self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase__:   Optional[Any]					=      ['''pixel_values''']
          self.assertListEqual(arg_names[:1]    ,			_UpperCAmelCase							)






    @slow
    def _snake_case			(   self							):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
          lowercase__:   List[Any]					=      MaskaFormerModel.from_pretrained(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   int					=      (self.model_tester.min_size,) * 2
       lowercase__:   int					=      {
           '''pixel_values''': torch.randn((2, 3, *size)    ,			device=_UpperCAmelCase							),
           '''mask_labels''': torch.randn((2, 10, *size)    ,			device=_UpperCAmelCase							),
           '''class_labels''': torch.zeros(2    ,			10    ,			device=_UpperCAmelCase							).long(),
       }
       lowercase__:   Tuple					=      self.model_tester.get_config()

       lowercase__:   Optional[int]					=      MaskaFormerForUniversalSegmentation(_UpperCAmelCase							).to(_UpperCAmelCase							)
       lowercase__:   Dict					=      model(**_UpperCAmelCase							)
       self.assertTrue(outputs.loss is not None							)






    def _snake_case			(   self							):
       lowercase__, lowercase__:   List[Any]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase    ,			**_UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__, lowercase__:   str					=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:   Union[str, Any]					=      model_class(_UpperCAmelCase							).to(_UpperCAmelCase							)
          lowercase__:   Union[str, Any]					=      model(**_UpperCAmelCase    ,			output_attentions=_UpperCAmelCase							)
          self.assertTrue(outputs.attentions is not None							)






    def _snake_case			(   self							):
       if not self.model_tester.is_training:
          return

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

       lowercase__:   Any					=      model_class(_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.train()

       lowercase__:   Dict					=      model(_UpperCAmelCase    ,			mask_labels=_UpperCAmelCase    ,			class_labels=_UpperCAmelCase							).loss
       loss.backward()






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.all_model_classes[1]
       lowercase__, lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      self.model_tester.prepare_config_and_inputs()
       lowercase__:   List[str]					=      True
       lowercase__:   Union[str, Any]					=      True

       lowercase__:   Optional[Any]					=      model_class(_UpperCAmelCase							).to(_UpperCAmelCase							)
       model.train()

       lowercase__:   List[Any]					=      model(_UpperCAmelCase    ,			mask_labels=_UpperCAmelCase    ,			class_labels=_UpperCAmelCase							)

       lowercase__:   List[Any]					=      outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       lowercase__:   int					=      outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

       lowercase__:   Optional[int]					=      outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       lowercase__:   Any					=      outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=_UpperCAmelCase							)

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



__A			   = 1E-4



def SCREAMING_SNAKE_CASE__			(   )  ->  int:
   lowercase__:   Tuple					=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
   return image




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



    """simple docstring"""

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






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






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      self.default_image_processor
       lowercase__:   Optional[int]					=      prepare_img()
       lowercase__:   Union[str, Any]					=      image_processor(_UpperCAmelCase    ,			return_tensors='''pt'''							).to(_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      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(_UpperCAmelCase    ,			(1, 3, 384, 384)							)

       with torch.no_grad():
          lowercase__:   Any					=      model(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      torch.tensor(
           [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]							).to(_UpperCAmelCase							)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,			_UpperCAmelCase    ,			atol=_UpperCAmelCase							)							)

       lowercase__:   Dict					=      torch.tensor(
           [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]							).to(_UpperCAmelCase							)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,			_UpperCAmelCase    ,			atol=_UpperCAmelCase							)							)

       lowercase__:   Any					=      torch.tensor(
           [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]							).to(_UpperCAmelCase							)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,			_UpperCAmelCase    ,			atol=_UpperCAmelCase							)							)






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(_UpperCAmelCase							).eval()
       lowercase__:   int					=      self.default_image_processor
       lowercase__:   List[str]					=      prepare_img()
       lowercase__:   Any					=      image_processor(_UpperCAmelCase    ,			return_tensors='''pt'''							).to(_UpperCAmelCase							)
       lowercase__:   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(_UpperCAmelCase    ,			(1, 3, 384, 384)							)

       with torch.no_grad():
          lowercase__:   Any					=      model(**_UpperCAmelCase							)
       # masks_queries_logits
       lowercase__:   Any					=      outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape    ,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)							)
       lowercase__:   Tuple					=      [
           [-8.7_839, -9.0_056, -8.8_121],
           [-7.4_104, -7.0_313, -6.5_401],
           [-6.6_105, -6.3_427, -6.4_675],
       ]
       lowercase__:   List[str]					=      torch.tensor(_UpperCAmelCase							).to(_UpperCAmelCase							)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,			_UpperCAmelCase    ,			atol=_UpperCAmelCase							)							)
       # class_queries_logits
       lowercase__:   str					=      outputs.class_queries_logits
       self.assertEqual(class_queries_logits.shape    ,			(1, model.config.num_queries, model.config.num_labels + 1)							)
       lowercase__:   Optional[Any]					=      torch.tensor(
           [
               [1.8_324, -8.0_835, -4.1_922],
               [0.8_450, -9.0_050, -3.6_053],
               [0.3_045, -7.7_293, -3.0_275],
           ]							).to(_UpperCAmelCase							)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,			_UpperCAmelCase    ,			atol=_UpperCAmelCase							)							)






    def _snake_case			(   self							):
       lowercase__:   str					=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(_UpperCAmelCase							).eval()
       lowercase__:   Union[str, Any]					=      self.default_image_processor

       lowercase__:   Optional[Any]					=      image_processor(
           [np.zeros((3, 800, 1333)							), np.zeros((3, 800, 1333)							)]    ,			segmentation_maps=[np.zeros((384, 384)							).astype(np.floataa							), np.zeros((384, 384)							).astype(np.floataa							)]    ,			return_tensors='''pt'''    ,			)

       lowercase__:   Optional[Any]					=      inputs['''pixel_values'''].to(_UpperCAmelCase							)
       lowercase__:   Tuple					=      [el.to(_UpperCAmelCase							) for el in inputs['''mask_labels''']]
       lowercase__:   int					=      [el.to(_UpperCAmelCase							) for el in inputs['''class_labels''']]

       with torch.no_grad():
          lowercase__:   Tuple					=      model(**_UpperCAmelCase							)

       self.assertTrue(outputs.loss is not None							)

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
