




import argparse
import os
import re


lowerCamelCase__			:       str         =							'src/transformers'

# Pattern that looks at the indentation in a line.
lowerCamelCase__			:       Tuple         =							re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__			:       str         =							re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'\[([^\]]+)\]')

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Any:
      SCREAMING_SNAKE_CASE_     						=      _re_indent.search(__UpperCAmelCase							)
      return "" if search is None else search.groups()[0]

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Optional[int]=""      ,      __UpperCAmelCase						:     Optional[int]=None      ,      __UpperCAmelCase						:     Dict=None							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      0
      SCREAMING_SNAKE_CASE_     						=      code.split('\n'							)
      if start_prompt is not None:
            while not lines[index].startswith(__UpperCAmelCase							):
                  index += 1
            SCREAMING_SNAKE_CASE_     						=      ['\n'.join(lines[:index]							)]
      else:
            SCREAMING_SNAKE_CASE_     						=      []

      # We split into blocks until we get to the `end_prompt` (or the end of the block).
      SCREAMING_SNAKE_CASE_     						=      [lines[index]]
      index += 1
      while index < len(__UpperCAmelCase							) and (end_prompt is None or not lines[index].startswith(__UpperCAmelCase							)):
            if len(lines[index]							) > 0 and get_indent(lines[index]							) == indent_level:
                  if len(__UpperCAmelCase							) > 0 and get_indent(current_block[-1]							).startswith(indent_level + ' '							):
                        current_block.append(lines[index]							)
                        blocks.append('\n'.join(__UpperCAmelCase							)							)
                        if index < len(__UpperCAmelCase							) - 1:
                              SCREAMING_SNAKE_CASE_     						=      [lines[index + 1]]
                              index += 1
                        else:
                              SCREAMING_SNAKE_CASE_     						=      []
                  else:
                        blocks.append('\n'.join(__UpperCAmelCase							)							)
                        SCREAMING_SNAKE_CASE_     						=      [lines[index]]
            else:
                  current_block.append(lines[index]							)
            index += 1

      # Adds current block if it's nonempty.
      if len(__UpperCAmelCase							) > 0:
            blocks.append('\n'.join(__UpperCAmelCase							)							)

      # Add final block after end_prompt if provided.
      if end_prompt is not None and index < len(__UpperCAmelCase							):
            blocks.append('\n'.join(lines[index:]							)							)

      return blocks

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict							) ->			Any:

      def _inner(__UpperCAmelCase						:     Tuple							):
            return key(__UpperCAmelCase							).lower().replace('_'      ,      ''							)

      return _inner

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Union[str, Any]=None							) ->			Dict:

      # If no key is provided, we use a noop.
      def noop(__UpperCAmelCase						:     Dict							):
            return x

      if key is None:
            SCREAMING_SNAKE_CASE_     						=      noop
      # Constants are all uppercase, they go first.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if key(__UpperCAmelCase							).isupper()]
      # Classes are not all uppercase but start with a capital, they go second.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if key(__UpperCAmelCase							)[0].isupper() and not key(__UpperCAmelCase							).isupper()]
      # Functions begin with a lowercase, they go last.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if not key(__UpperCAmelCase							)[0].isupper()]

      SCREAMING_SNAKE_CASE_     						=      ignore_underscore(__UpperCAmelCase							)
      return sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							) + sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							) + sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple							) ->			List[Any]:

      # This inner function sort imports between [ ].
      def _replace(__UpperCAmelCase						:     Optional[Any]							):
            SCREAMING_SNAKE_CASE_     						=      match.groups()[0]
            if "," not in imports:
                  return f"[{imports}]"
            SCREAMING_SNAKE_CASE_     						=      [part.strip().replace('"'      ,      ''							) for part in imports.split(','							)]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]							) == 0:
                  SCREAMING_SNAKE_CASE_     						=      keys[:-1]
            return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(__UpperCAmelCase							)]							) + "]"

      SCREAMING_SNAKE_CASE_     						=      import_statement.split('\n'							)
      if len(__UpperCAmelCase							) > 3:
            # Here we have to sort internal imports that are on several lines (one per name):
            # key: [
            #     "object1",
            #     "object2",
            #     ...
            # ]

            # We may have to ignore one or two lines on each side.
            SCREAMING_SNAKE_CASE_     						=      2 if lines[1].strip() == '[' else 1
            SCREAMING_SNAKE_CASE_     						=      [(i, _re_strip_line.search(__UpperCAmelCase							).groups()[0]) for i, line in enumerate(lines[idx:-idx]							)]
            SCREAMING_SNAKE_CASE_     						=      sort_objects(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase							: x[1]							)
            SCREAMING_SNAKE_CASE_     						=      [lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]							)
      elif len(__UpperCAmelCase							) == 3:
            # Here we have to sort internal imports that are on one separate line:
            # key: [
            #     "object1", "object2", ...
            # ]
            if _re_bracket_content.search(lines[1]							) is not None:
                  SCREAMING_SNAKE_CASE_     						=      _re_bracket_content.sub(_replace      ,      lines[1]							)
            else:
                  SCREAMING_SNAKE_CASE_     						=      [part.strip().replace('"'      ,      ''							) for part in lines[1].split(','							)]
                  # We will have a final empty element if the line finished with a comma.
                  if len(keys[-1]							) == 0:
                        SCREAMING_SNAKE_CASE_     						=      keys[:-1]
                  SCREAMING_SNAKE_CASE_     						=      get_indent(lines[1]							) + ', '.join([f"\"{k}\"" for k in sort_objects(__UpperCAmelCase							)]							)
            return "\n".join(__UpperCAmelCase							)
      else:
            # Finally we have to deal with imports fitting on one line
            SCREAMING_SNAKE_CASE_     						=      _re_bracket_content.sub(_replace      ,      __UpperCAmelCase							)
            return import_statement

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Any=True							) ->			Any:
      with open(__UpperCAmelCase      ,      encoding='utf-8'							) as f:
            SCREAMING_SNAKE_CASE_     						=      f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      SCREAMING_SNAKE_CASE_     						=      split_code_in_indented_blocks(
          __UpperCAmelCase      ,      start_prompt='_import_structure = {'      ,      end_prompt='if TYPE_CHECKING:'							)

      # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
      for block_idx in range(1      ,      len(__UpperCAmelCase							) - 1							):
            # Check if the block contains some `_import_structure`s thingy to sort.
            SCREAMING_SNAKE_CASE_     						=      main_blocks[block_idx]
            SCREAMING_SNAKE_CASE_     						=      block.split('\n'							)

            # Get to the start of the imports.
            SCREAMING_SNAKE_CASE_     						=      0
            while line_idx < len(__UpperCAmelCase							) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        SCREAMING_SNAKE_CASE_     						=      len(__UpperCAmelCase							)
                  else:
                        line_idx += 1
            if line_idx >= len(__UpperCAmelCase							):
                  continue

            # Ignore beginning and last line: they don't contain anything.
            SCREAMING_SNAKE_CASE_     						=      '\n'.join(block_lines[line_idx:-1]							)
            SCREAMING_SNAKE_CASE_     						=      get_indent(block_lines[1]							)
            # Slit the internal block into blocks of indent level 1.
            SCREAMING_SNAKE_CASE_     						=      split_code_in_indented_blocks(__UpperCAmelCase      ,      indent_level=__UpperCAmelCase							)
            # We have two categories of import key: list or _import_structure[key].append/extend
            SCREAMING_SNAKE_CASE_     						=      _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
            # Grab the keys, but there is a trap: some lines are empty or just comments.
            SCREAMING_SNAKE_CASE_     						=      [(pattern.search(__UpperCAmelCase							).groups()[0] if pattern.search(__UpperCAmelCase							) is not None else None) for b in internal_blocks]
            # We only sort the lines with a key.
            SCREAMING_SNAKE_CASE_     						=      [(i, key) for i, key in enumerate(__UpperCAmelCase							) if key is not None]
            SCREAMING_SNAKE_CASE_     						=      [x[0] for x in sorted(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase							: x[1]							)]

            # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
            SCREAMING_SNAKE_CASE_     						=      0
            SCREAMING_SNAKE_CASE_     						=      []
            for i in range(len(__UpperCAmelCase							)							):
                  if keys[i] is None:
                        reorderded_blocks.append(internal_blocks[i]							)
                  else:
                        SCREAMING_SNAKE_CASE_     						=      sort_objects_in_import(internal_blocks[sorted_indices[count]]							)
                        reorderded_blocks.append(__UpperCAmelCase							)
                        count += 1

        # And we put our main block back together with its first and last line.
            SCREAMING_SNAKE_CASE_     						=      '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]							)

      if code != "\n".join(__UpperCAmelCase							):
            if check_only:
                  return True
            else:
                  print(f"Overwriting {file}."							)
                  with open(__UpperCAmelCase      ,      'w'      ,      encoding='utf-8'							) as f:
                        f.write('\n'.join(__UpperCAmelCase							)							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]=True							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      []
      for root, _, files in os.walk(__UpperCAmelCase							):
            if "__init__.py" in files:
                  SCREAMING_SNAKE_CASE_     						=      sort_imports(os.path.join(__UpperCAmelCase      ,      '__init__.py'							)      ,      check_only=__UpperCAmelCase							)
                  if result:
                        SCREAMING_SNAKE_CASE_     						=      [os.path.join(__UpperCAmelCase      ,      '__init__.py'							)]
      if len(__UpperCAmelCase							) > 0:
            raise ValueError(f"Would overwrite {len(__UpperCAmelCase							)} files, run `make style`."							)


if __name__ == "__main__":
   lowerCamelCase__			:       int         =							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   lowerCamelCase__			:       Optional[Any]         =							parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)





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 	lowerCamelCase_							:


 '''simple docstring'''







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



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
           _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,    device=_lowerCAmelCase						)

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

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



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerConfig(
           hidden_size=self.hidden_dim    ,    )
       SCREAMING_SNAKE_CASE_     						=      self.num_queries
       SCREAMING_SNAKE_CASE_     						=      self.num_labels

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

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



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
       return config, inputs_dict



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      output.encoder_hidden_states
       SCREAMING_SNAKE_CASE_     						=      output.pixel_decoder_hidden_states
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Any=False						):
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel(config=_lowerCAmelCase						)
             model.to(_lowerCAmelCase						)
             model.eval()

             SCREAMING_SNAKE_CASE_     						=      model(pixel_values=_lowerCAmelCase    ,    pixel_mask=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()

       def comm_check_on_output(_lowerCAmelCase  :		List[str]						):
             # 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():
             SCREAMING_SNAKE_CASE_     						=      model(pixel_values=_lowerCAmelCase    ,    pixel_mask=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)

             comm_check_on_output(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       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 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerModelTester(self						)
       SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase    ,    has_text_modality=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       self.config_tester.run_common_tests()



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase    ,    **_lowerCAmelCase    ,    output_hidden_states=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[int]						):
       pass



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



 @unittest.skip(reason='Mask2Former is not a generative model'						)
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       pass



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



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



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



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_     						=      ['pixel_values']
             self.assertListEqual(arg_names[:1]    ,    _lowerCAmelCase						)



 @slow
 def 				lowerCAmelCase_  (							self  :		Any						):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      (self.model_tester.min_size,) * 2
       SCREAMING_SNAKE_CASE_     						=      {
           '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(),
       }
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.get_config()

       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation(_lowerCAmelCase						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)
       self.assertTrue(outputs.loss is not None						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase    ,    **_lowerCAmelCase    ,    output_hidden_states=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       if not self.model_tester.is_training:
             return

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

       SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.train()

       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    mask_labels=_lowerCAmelCase    ,    class_labels=_lowerCAmelCase						).loss
       loss.backward()




 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_     						=      True
       SCREAMING_SNAKE_CASE_     						=      True

       SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						).to(_lowerCAmelCase						)
       model.train()

       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    mask_labels=_lowerCAmelCase    ,    class_labels=_lowerCAmelCase						)

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

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

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

       SCREAMING_SNAKE_CASE_     						=      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						)


lowerCamelCase__			:       Tuple         =							1E-4

def  UpperCAmelCase_							(				) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





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


 '''simple docstring'''







 @cached_property
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       return "facebook/mask2former-swin-small-coco-instance"



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



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.default_image_processor
       SCREAMING_SNAKE_CASE_     						=      prepare_img()
       SCREAMING_SNAKE_CASE_     						=      image_processor(_lowerCAmelCase    ,    return_tensors='pt'						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      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():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      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						)						)

       SCREAMING_SNAKE_CASE_     						=      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						)						)

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

       SCREAMING_SNAKE_CASE_     						=      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'    ,    )

       SCREAMING_SNAKE_CASE_     						=      inputs['pixel_values'].to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      [el.to(_lowerCAmelCase						) for el in inputs['mask_labels']]
       SCREAMING_SNAKE_CASE_     						=      [el.to(_lowerCAmelCase						) for el in inputs['class_labels']]

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)

       self.assertTrue(outputs.loss is not None						)