


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

 if is_vision_available():
  from transformers import MaskFormerImageProcessor

if is_vision_available():
 from PIL import Image

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=2     ,   __snake_case=True     ,   __snake_case=False     ,   __snake_case=1_0     ,   __snake_case=3     ,   __snake_case=3_2 * 4     ,   __snake_case=3_2 * 6     ,   __snake_case=4     ,   __snake_case=3_2     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							is_training
         snake_case											=							use_auxiliary_loss
         snake_case											=							num_queries
         snake_case											=							num_channels
         snake_case											=							min_size
         snake_case											=							max_size
         snake_case											=							num_labels
         snake_case											=							mask_feature_size
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
             __snake_case       )

         snake_case											=							torch.ones([self.batch_size, self.min_size, self.max_size]     ,   device=__snake_case       )

         snake_case											=							(
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,   device=__snake_case       ) > 0.5
         ).float()
         snake_case											=							(torch.rand((self.batch_size, self.num_labels)     ,   device=__snake_case       ) > 0.5).long()

         snake_case											=							self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels
    def 						a_     (			self       ):
         return MaskFormerConfig.from_backbone_and_decoder_configs(
             backbone_config=SwinConfig(
                 depths=[1, 1, 1, 1]     ,   )     ,   decoder_config=DetrConfig(
                 decoder_ffn_dim=1_2_8     ,   num_queries=self.num_queries     ,   decoder_attention_heads=2     ,   d_model=self.mask_feature_size     ,   )     ,   mask_feature_size=self.mask_feature_size     ,   fpn_feature_size=self.mask_feature_size     ,   num_channels=self.num_channels     ,   num_labels=self.num_labels     ,   )
    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         snake_case											=							{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
         return config, inputs_dict
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							output.encoder_hidden_states
         snake_case											=							output.pixel_decoder_hidden_states
         snake_case											=							output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(__snake_case       )     ,   len(config.backbone_config.depths       )       )
         self.parent.assertTrue(len(__snake_case       )     ,   len(config.backbone_config.depths       )       )
         self.parent.assertTrue(len(__snake_case       )     ,   config.decoder_config.decoder_layers       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case=False       ):






         with torch.no_grad():
              snake_case											=							MaskFormerModel(config=__snake_case       )
              model.to(__snake_case       )
              model.eval()

              snake_case											=							model(pixel_values=__snake_case     ,   pixel_mask=__snake_case       )
              snake_case											=							model(__snake_case     ,   output_hidden_states=__snake_case       )
         # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
         # encoder and pixel decoder
         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape     ,   (self.batch_size, self.num_queries, self.mask_feature_size)     ,   )
         # 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(__snake_case     ,   __snake_case       )




    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							MaskFormerForInstanceSegmentation(config=__snake_case       )
         model.to(__snake_case       )
         model.eval()

         def comm_check_on_output(__snake_case       ):
              # 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():
              snake_case											=							model(pixel_values=__snake_case     ,   pixel_mask=__snake_case       )
              snake_case											=							model(__snake_case       )

              comm_check_on_output(__snake_case       )

              snake_case											=							model(
                  pixel_values=__snake_case     ,   pixel_mask=__snake_case     ,   mask_labels=__snake_case     ,   class_labels=__snake_case       )

         comm_check_on_output(__snake_case       )

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






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







    """simple docstring"""

    __magic_name__         =						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    __magic_name__         =						(
        {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self       ):
         snake_case											=							MaskFormerModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__snake_case     ,   **__snake_case     ,   output_hidden_states=__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__snake_case       )
    @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip(reason='''MaskFormer is not a generative model'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip(reason='''MaskFormer does not use token embeddings'''       )
    def 						a_     (			self       ):
         pass
    @require_torch_multi_gpu
    @unittest.skip(
        reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							["pixel_values"]
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    @slow
    def 						a_     (			self       ):






         for model_name in ["facebook/maskformer-swin-small-coco"]:
              snake_case											=							MaskFormerModel.from_pretrained(__snake_case       )
              self.assertIsNotNone(__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							(self.model_tester.min_size,) * 2
         snake_case											=							{
             "pixel_values": torch.randn((2, 3, *size)     ,   device=__snake_case       ),
             "mask_labels": torch.randn((2, 1_0, *size)     ,   device=__snake_case       ),
             "class_labels": torch.zeros(2     ,   1_0     ,   device=__snake_case       ).long(),
         }

         snake_case											=							MaskFormerForInstanceSegmentation(MaskFormerConfig()       ).to(__snake_case       )
         snake_case											=							model(**__snake_case       )
         self.assertTrue(outputs.loss is not None       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__snake_case     ,   **__snake_case     ,   output_hidden_states=__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       ).to(__snake_case       )
              snake_case											=							model(**__snake_case     ,   output_attentions=__snake_case       )
              self.assertTrue(outputs.attentions is not None       )
    def 						a_     (			self       ):






         if not self.model_tester.is_training:
              return
         # only MaskFormerForInstanceSegmentation has the loss
         snake_case											=							self.all_model_classes[1]
         snake_case											=							self.model_tester.prepare_config_and_inputs()

         snake_case											=							model_class(__snake_case       )
         model.to(__snake_case       )
         model.train()

         snake_case											=							model(__snake_case     ,   mask_labels=__snake_case     ,   class_labels=__snake_case       ).loss
         loss.backward()




    def 						a_     (			self       ):
         snake_case											=							self.all_model_classes[1]
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         snake_case											=							True
         snake_case											=							True

         snake_case											=							model_class(__snake_case       )
         model.to(__snake_case       )
         model.train()

         snake_case											=							model(__snake_case     ,   mask_labels=__snake_case     ,   class_labels=__snake_case       )

         snake_case											=							outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         snake_case											=							outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()
         # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
         snake_case											=							outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         snake_case											=							outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=__snake_case       )

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






_SCREAMING_SNAKE_CASE     :					Tuple      =       1e-4
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image

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







    """simple docstring"""

    @cached_property
    def 						a_     (			self       ):
         return (
             MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''       )
             if is_vision_available()
             else None
         )
    def 						a_     (			self       ):
         snake_case											=							MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''       ).to(__snake_case       )
         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(__snake_case     ,   return_tensors='''pt'''       ).to(__snake_case       )
         snake_case											=							inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
         # check size
         self.assertEqual(__snake_case     ,   (1, 3, 8_0_0, 1_0_8_8)       )

         with torch.no_grad():
              snake_case											=							model(**__snake_case       )

         snake_case											=							torch.tensor(
             [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]       ).to(__snake_case       )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )

         snake_case											=							torch.tensor(
             [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]       ).to(__snake_case       )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )

         snake_case											=							torch.tensor(
             [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]       ).to(__snake_case       )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )
    def 						a_     (			self       ):
         snake_case											=							(
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''       )
             .to(__snake_case       )
             .eval()
         )
         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(__snake_case     ,   return_tensors='''pt'''       ).to(__snake_case       )
         snake_case											=							inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
         # check size
         self.assertEqual(__snake_case     ,   (1, 3, 8_0_0, 1_0_8_8)       )

         with torch.no_grad():
              snake_case											=							model(**__snake_case       )
         # masks_queries_logits
         snake_case											=							outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape     ,   (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     ,   )
         snake_case											=							[
             [-1.373_7124, -1.772_4937, -1.936_4233],
             [-1.597_7281, -1.986_7939, -2.152_3695],
             [-1.579_5398, -1.926_9832, -2.09_3942],
         ]
         snake_case											=							torch.tensor(__snake_case       ).to(__snake_case       )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )
         # class_queries_logits
         snake_case											=							outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape     ,   (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
         snake_case											=							torch.tensor(
             [
                 [1.65_12E00, -5.25_72E00, -3.35_19E00],
                 [3.61_69E-02, -5.90_25E00, -2.93_13E00],
                 [1.07_66E-04, -7.76_30E00, -5.12_63E00],
             ]       ).to(__snake_case       )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )
    def 						a_     (			self       ):
         snake_case											=							(
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''       )
             .to(__snake_case       )
             .eval()
         )
         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(__snake_case     ,   return_tensors='''pt'''       ).to(__snake_case       )
         snake_case											=							inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
         # check size
         self.assertEqual(__snake_case     ,   (1, 3, 8_0_0, 1_0_8_8)       )

         with torch.no_grad():
              snake_case											=							model(**__snake_case       )
         # masks_queries_logits
         snake_case											=							outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape     ,   (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     ,   )
         snake_case											=							[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
         snake_case											=							torch.tensor(__snake_case       ).to(__snake_case       )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )
         # class_queries_logits
         snake_case											=							outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape     ,   (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
         snake_case											=							torch.tensor(
             [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]       ).to(__snake_case       )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,   __snake_case     ,   atol=__snake_case       )       )




    def 						a_     (			self       ):
         snake_case											=							(
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''       )
             .to(__snake_case       )
             .eval()
         )
         snake_case											=							self.default_image_processor

         snake_case											=							image_processor(
             [np.zeros((3, 8_0_0, 1_3_3_3)       ), np.zeros((3, 8_0_0, 1_3_3_3)       )]     ,   segmentation_maps=[np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       ), np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       )]     ,   return_tensors='''pt'''     ,   )

         snake_case											=							inputs["pixel_values"].to(__snake_case       )
         snake_case											=							[el.to(__snake_case       ) for el in inputs["mask_labels"]]
         snake_case											=							[el.to(__snake_case       ) for el in inputs["class_labels"]]

         with torch.no_grad():
              snake_case											=							model(**__snake_case       )

         self.assertTrue(outputs.loss is not None       )












'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[Any]							     =  hidden_states.shape
       lowercase_						:     Tuple							     =  jax.image.resize(
           a	,  shape=(batch, height * 2, width * 2, channels)	,  method="nearest"	,  )
       lowercase_						:     List[Any]							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(2, 2)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: int									=     None
    __lowerCamelCase: float									=     0.0
    __lowerCamelCase: bool									=     None
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.in_channels if self.out_channels is None else self.out_channels

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Tuple							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     List[str]							     =  nn.Dense(a	,  dtype=self.dtype      )

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Any							     =  nn.Dropout(self.dropout_prob      )
       lowercase_						:     Dict							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     Tuple							     =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       lowercase_						:     Optional[Any]							     =  None
       if use_nin_shortcut:
          lowercase_						:     Union[str, Any]							     =  nn.Conv(
              a	,  kernel_size=(1, 1)	,  strides=(1, 1)	,  padding="VALID"	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							str	,  a	:							Dict	,  a	:							List[str]=True      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  hidden_states
       lowercase_						:     int							     =  self.norma(a      )
       lowercase_						:     List[Any]							     =  nn.swish(a      )
       lowercase_						:     Dict							     =  self.conva(a      )

       lowercase_						:     Optional[int]							     =  self.time_emb_proj(nn.swish(a      )      )
       lowercase_						:     Tuple							     =  jnp.expand_dims(jnp.expand_dims(a	,  1      )	,  1      )
       lowercase_						:     List[str]							     =  hidden_states + temb

       lowercase_						:     Optional[Any]							     =  self.norma(a      )
       lowercase_						:     Any							     =  nn.swish(a      )
       lowercase_						:     List[str]							     =  self.dropout(a	,  a      )
       lowercase_						:     int							     =  self.conva(a      )

       if self.conv_shortcut is not None:
          lowercase_						:     List[str]							     =  self.conv_shortcut(a      )

       return hidden_states + residual




