




import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def      A      (      _SCREAMING_SNAKE_CASE    )	-> List[Tuple[int, ...]]:
       lowerCamelCase   :      Tuple			 =					[]
       if isinstance(UpperCamelCase__ ,UpperCamelCase__    ):
              for v in tree.values():
                     shapes.extend(_fetch_dims(UpperCamelCase__    )    )
       elif isinstance(UpperCamelCase__ ,(list, tuple)    ):
              for t in tree:
                     shapes.extend(_fetch_dims(UpperCamelCase__    )    )
       elif isinstance(UpperCamelCase__ ,torch.Tensor    ):
              shapes.append(tree.shape    )
       else:
              raise ValueError("Not supported"    )

       return shapes

@torch.jit.ignore
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Tuple[int, ...]:
       lowerCamelCase   :      int			 =					[]
       for d in reversed(UpperCamelCase__    ):
              idx.append(flat_idx % d    )
              lowerCamelCase   :      Optional[int]			 =					flat_idx // d

       return tuple(reversed(UpperCamelCase__    )    )

@torch.jit.ignore
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[Tuple[slice, ...]]:

       # start_edges and end_edges both indicate whether, starting from any given
       # dimension, the start/end index is at the top/bottom edge of the
       # corresponding tensor, modeled as a tree
       def reduce_edge_list(_SCREAMING_SNAKE_CASE    ) -> None:
              lowerCamelCase   :      Optional[Any]			 =					True
              for i in range(len(UpperCamelCase__    )    ):
                     lowerCamelCase   :      List[str]			 =					-1 * (i + 1)
                     l[reversed_idx] &= tally
                     lowerCamelCase   :      Dict			 =					l[reversed_idx]

       if start_edges is None:
              lowerCamelCase   :      int			 =					[s == 0 for s in start]
              reduce_edge_list(UpperCamelCase__    )
       if end_edges is None:
              lowerCamelCase   :      Tuple			 =					[e == (d - 1) for e, d in zip(UpperCamelCase__ ,UpperCamelCase__    )]
              reduce_edge_list(UpperCamelCase__    )

       # Base cases. Either start/end are empty and we're done, or the final,
       # one-dimensional tensor can be simply sliced
       if len(UpperCamelCase__    ) == 0:
              return [()]
       elif len(UpperCamelCase__    ) == 1:
              return [(slice(start[0] ,end[0] + 1    ),)]

       lowerCamelCase   :      List[Any]			 =					[]
       lowerCamelCase   :      Optional[Any]			 =					[]

       # Dimensions common to start and end can be selected directly
       for s, e in zip(UpperCamelCase__ ,UpperCamelCase__    ):
              if s == e:
                     path_list.append(slice(UpperCamelCase__ ,s + 1    )    )
              else:
                     break

       lowerCamelCase   :      Any			 =					tuple(UpperCamelCase__    )
       lowerCamelCase   :      str			 =					len(UpperCamelCase__    )

       # start == end, and we're done
       if divergence_idx == len(UpperCamelCase__    ):
              return [path]

       def upper() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              lowerCamelCase   :      Dict			 =					start[divergence_idx]
              return tuple(
                  path + (slice(UpperCamelCase__ ,sdi + 1    ),) + s
                  for s in _get_minimal_slice_set(
                      start[divergence_idx + 1 :] ,[d - 1 for d in dims[divergence_idx + 1 :]] ,dims[divergence_idx + 1 :] ,start_edges=start_edges[divergence_idx + 1 :] ,end_edges=[True for _ in end_edges[divergence_idx + 1 :]] ,)    )

       def lower() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              lowerCamelCase   :      Dict			 =					end[divergence_idx]
              return tuple(
                  path + (slice(UpperCamelCase__ ,edi + 1    ),) + s
                  for s in _get_minimal_slice_set(
                      [0 for _ in start[divergence_idx + 1 :]] ,end[divergence_idx + 1 :] ,dims[divergence_idx + 1 :] ,start_edges=[True for _ in start_edges[divergence_idx + 1 :]] ,end_edges=end_edges[divergence_idx + 1 :] ,)    )

       # If both start and end are at the edges of the subtree rooted at
       # divergence_idx, we can just select the whole subtree at once
       if start_edges[divergence_idx] and end_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx] ,end[divergence_idx] + 1    ),)    )
       # If just start is at the edge, we can grab almost all of the subtree,
       # treating only the ragged bottom edge as an edge case
       elif start_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx] ,end[divergence_idx]    ),)    )
              slices.extend(lower()    )
       # Analogous to the previous case, but the top is ragged this time
       elif end_edges[divergence_idx]:
              slices.extend(upper()    )
              slices.append(path + (slice(start[divergence_idx] + 1 ,end[divergence_idx] + 1    ),)    )
       # If both sides of the range are ragged, we need to handle both sides
       # separately. If there's contiguous meat in between them, we can index it
       # in one big chunk
       else:
              slices.extend(upper()    )
              lowerCamelCase   :      Tuple			 =					end[divergence_idx] - start[divergence_idx]
              if middle_ground > 1:
                     slices.append(path + (slice(start[divergence_idx] + 1 ,end[divergence_idx]    ),)    )
              slices.extend(lower()    )

       return slices

@torch.jit.ignore
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> torch.Tensor:
       lowerCamelCase   :      str			 =					t.shape[:no_batch_dims]
       lowerCamelCase   :      Tuple			 =					list(_flat_idx_to_idx(UpperCamelCase__ ,UpperCamelCase__    )    )
       # _get_minimal_slice_set is inclusive
       lowerCamelCase   :      Any			 =					list(_flat_idx_to_idx(flat_end - 1 ,UpperCamelCase__    )    )

       # Get an ordered list of slices to perform
       lowerCamelCase   :      int			 =					_get_minimal_slice_set(
           UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ ,)

       lowerCamelCase   :      Dict			 =					[t[s] for s in slices]

       return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]    ) for s in sliced_tensors]    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,)	-> Any:
       if not (len(UpperCamelCase__    ) > 0):
              raise ValueError("Must provide at least one input"    )

       lowerCamelCase   :      Optional[int]			 =					[shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase__    )]
       lowerCamelCase   :      Any			 =					tuple([max(UpperCamelCase__    ) for s in zip(*UpperCamelCase__    )]    )

       def _prep_inputs(_SCREAMING_SNAKE_CASE    ) -> torch.Tensor:
              if not low_mem:
                     if not sum(t.shape[:no_batch_dims]    ) == no_batch_dims:
                            lowerCamelCase   :      Optional[int]			 =					t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
                     lowerCamelCase   :      Optional[Any]			 =					t.reshape(-1 ,*t.shape[no_batch_dims:]    )
              else:
                     lowerCamelCase   :      Any			 =					t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
              return t

       lowerCamelCase   :      List[Any]			 =					tensor_tree_map(_prep_inputs ,UpperCamelCase__    )
       lowerCamelCase   :      List[str]			 =					None
       if _out is not None:
              lowerCamelCase   :      Optional[Any]			 =					tensor_tree_map(lambda _SCREAMING_SNAKE_CASE    : t.view([-1] + list(t.shape[no_batch_dims:]    )    ) ,_out    )

       lowerCamelCase   :      Optional[Any]			 =					1
       for d in orig_batch_dims:
              flat_batch_dim *= d

       lowerCamelCase   :      Dict			 =					flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

       def _select_chunk(_SCREAMING_SNAKE_CASE    ) -> torch.Tensor:
              return t[i : i + chunk_size] if t.shape[0] != 1 else t

       lowerCamelCase   :      str			 =					0
       lowerCamelCase   :      Any			 =					prepped_outputs
       for _ in range(UpperCamelCase__    ):
              # Chunk the input
              if not low_mem:
                     lowerCamelCase   :      List[str]			 =					_select_chunk
              else:
                     lowerCamelCase   :      int			 =					partial(
                         _chunk_slice ,flat_start=UpperCamelCase__ ,flat_end=min(UpperCamelCase__ ,i + chunk_size    ) ,no_batch_dims=len(UpperCamelCase__    ) ,)

              lowerCamelCase   :      Union[str, Any]			 =					tensor_tree_map(UpperCamelCase__ ,UpperCamelCase__    )

              # Run the layer on the chunk
              lowerCamelCase   :      int			 =					layer(**UpperCamelCase__    )

              # Allocate space for the output
              if out is None:
                     lowerCamelCase   :      Any			 =					tensor_tree_map(lambda _SCREAMING_SNAKE_CASE    : t.new_zeros((flat_batch_dim,) + t.shape[1:]    ) ,UpperCamelCase__    )

              # Put the chunk in its pre-allocated space
              if isinstance(UpperCamelCase__ ,UpperCamelCase__    ):

                     def assign(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) -> None:
                            for k, v in da.items():
                                   if isinstance(UpperCamelCase__ ,UpperCamelCase__    ):
                                          assign(UpperCamelCase__ ,da[k]    )
                                   else:
                                          if _add_into_out:
                                                 v[i : i + chunk_size] += da[k]
                                          else:
                                                 lowerCamelCase   :      Union[str, Any]			 =					da[k]

                     assign(UpperCamelCase__ ,UpperCamelCase__    )
              elif isinstance(UpperCamelCase__ ,UpperCamelCase__    ):
                     for xa, xa in zip(UpperCamelCase__ ,UpperCamelCase__    ):
                            if _add_into_out:
                                   xa[i : i + chunk_size] += xa
                            else:
                                   lowerCamelCase   :      str			 =					xa
              elif isinstance(UpperCamelCase__ ,torch.Tensor    ):
                     if _add_into_out:
                            out[i : i + chunk_size] += output_chunk
                     else:
                            lowerCamelCase   :      Optional[Any]			 =					output_chunk
              else:
                     raise ValueError("Not supported"    )

              i += chunk_size

       lowerCamelCase   :      Tuple			 =					tensor_tree_map(lambda _SCREAMING_SNAKE_CASE    : t.view(orig_batch_dims + t.shape[1:]    ) ,UpperCamelCase__    )

       return out




class 		UpperCamelCase__	:

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__ = 512     ,  )     ->      List[str]:
           lowerCamelCase   :      List[Any]			 =					max_chunk_size
           lowerCamelCase   :      str			 =					None
           lowerCamelCase   :      Optional[int]			 =					None







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[Any]:
           logging.info("Tuning chunk size..."     )

           if min_chunk_size >= self.max_chunk_size:
                  return min_chunk_size

           lowerCamelCase   :      Optional[int]			 =					[2**l for l in range(int(math.log(self.max_chunk_size     ,  2     )     ) + 1     )]
           lowerCamelCase   :      Dict			 =					[c for c in candidates if c > min_chunk_size]
           lowerCamelCase   :      List[Any]			 =					[min_chunk_size] + candidates
           candidates[-1] += 4

           def test_chunk_size(UpperCamelCase__     ) -> bool:
                  try:
                         with torch.no_grad():
                                fn(*__lowerCamelCase     ,  chunk_size=__lowerCamelCase     )
                         return True
                  except RuntimeError:
                         return False

           lowerCamelCase   :      Union[str, Any]			 =					0
           lowerCamelCase   :      List[Any]			 =					len(__lowerCamelCase     ) - 1
           while i > min_viable_chunk_size_index:
                  lowerCamelCase   :      Optional[Any]			 =					test_chunk_size(candidates[i]     )
                  if not viable:
                         lowerCamelCase   :      List[Any]			 =					(min_viable_chunk_size_index + i) // 2
                  else:
                         lowerCamelCase   :      Any			 =					i
                         lowerCamelCase   :      Tuple			 =					(i + len(__lowerCamelCase     ) - 1) // 2

           return candidates[min_viable_chunk_size_index]







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase   :      Union[str, Any]			 =					True
           for aa, aa in zip(__lowerCamelCase     ,  __lowerCamelCase     ):
                  assert type(__lowerCamelCase     ) == type(__lowerCamelCase     )
                  if isinstance(__lowerCamelCase     ,  (list, tuple)     ):
                         consistent &= self._compare_arg_caches(__lowerCamelCase     ,  __lowerCamelCase     )
                  elif isinstance(__lowerCamelCase     ,  __lowerCamelCase     ):
                         lowerCamelCase   :      Any			 =					[v for _, v in sorted(aa.items()     ,  key=lambda UpperCamelCase__     : x[0]     )]
                         lowerCamelCase   :      Any			 =					[v for _, v in sorted(aa.items()     ,  key=lambda UpperCamelCase__     : x[0]     )]
                         consistent &= self._compare_arg_caches(__lowerCamelCase     ,  __lowerCamelCase     )
                  else:
                         consistent &= aa == aa

           return consistent







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					True
           lowerCamelCase   :      Tuple			 =					tree_map(lambda UpperCamelCase__     : a.shape if isinstance(__lowerCamelCase     ,  torch.Tensor     ) else a     ,  __lowerCamelCase     ,  __lowerCamelCase     )
           if self.cached_arg_data is not None:
                  # If args have changed shape/value, we need to re-tune
                  assert len(self.cached_arg_data     ) == len(__lowerCamelCase     )
                  lowerCamelCase   :      str			 =					self._compare_arg_caches(self.cached_arg_data     ,  __lowerCamelCase     )
           else:
                  # Otherwise, we can reuse the precomputed value
                  lowerCamelCase   :      int			 =					False

           if not consistent:
                  lowerCamelCase   :      int			 =					self._determine_favorable_chunk_size(
                      __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  )
                  lowerCamelCase   :      Dict			 =					arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size













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     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=2,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=10,__lowerCamelCase=3,__lowerCamelCase=32 * 8,__lowerCamelCase=32 * 8,__lowerCamelCase=4,__lowerCamelCase=64,):
        A__											=						parent
        A__											=						batch_size
        A__											=						is_training
        A__											=						use_auxiliary_loss
        A__											=						num_queries
        A__											=						num_channels
        A__											=						min_size
        A__											=						max_size
        A__											=						num_labels
        A__											=						hidden_dim
        A__											=						hidden_dim



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
            __lowerCamelCase )

        A__											=						torch.ones([self.batch_size, self.min_size, self.max_size],device=__lowerCamelCase )

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

        A__											=						self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerConfig(
            hidden_size=self.hidden_dim,)
        A__											=						self.num_queries
        A__											=						self.num_labels

        A__											=						[1, 1, 1, 1]
        A__											=						self.num_channels

        A__											=						64
        A__											=						128
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        return config



 def 				UpperCamelCase			(							self ):
        A__					,	A__					,	A__					,	A__					,	A__											=						self.prepare_config_and_inputs()
        A__											=						{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
        return config, inputs_dict



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						output.encoder_hidden_states
        A__											=						output.pixel_decoder_hidden_states
        A__											=						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,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=False ):
        with torch.no_grad():
               A__											=						MaskaFormerModel(config=__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						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,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						MaskaFormerForUniversalSegmentation(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

        def comm_check_on_output(__lowerCamelCase ):
               # 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__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase )

               comm_check_on_output(__lowerCamelCase )

               A__											=						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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							       =	{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						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 ):
        A__											=						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 ):
        pass



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



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



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



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               A__											=						MaskaFormerModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						(self.model_tester.min_size,) * 2
        A__											=						{
            '''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__											=						self.model_tester.get_config()

        A__											=						MaskaFormerForUniversalSegmentation(__lowerCamelCase ).to(__lowerCamelCase )
        A__											=						model(**__lowerCamelCase )
        self.assertTrue(outputs.loss is not None )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						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 ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
               A__											=						model(**__lowerCamelCase,output_attentions=__lowerCamelCase )
               self.assertTrue(outputs.attentions is not None )



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

        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()

        A__											=						model_class(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase ).loss
        loss.backward()




 def 				UpperCamelCase			(							self ):
        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()
        A__											=						True
        A__											=						True

        A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

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

        A__											=						outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        A__											=						outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        A__											=						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 )
a__:      str									=		1e-4


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




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



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



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



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase )
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						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__											=						model(**__lowerCamelCase )

        A__											=						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__											=						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__											=						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 ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						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__											=						model(**__lowerCamelCase )
        # masks_queries_logits
        A__											=						outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
        A__											=						[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        A__											=						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )
        # class_queries_logits
        A__											=						outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape,(1, model.config.num_queries, model.config.num_labels + 1) )
        A__											=						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 ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor

        A__											=						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''',)

        A__											=						inputs['''pixel_values'''].to(__lowerCamelCase )
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''mask_labels''']]
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''class_labels''']]

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        self.assertTrue(outputs.loss is not None )





