
'''simple docstring'''







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase         =    logging.getLogger(__name__)







@dataclass
class UpperCAmelCase__	:





  """simple docstring"""

  __UpperCAmelCase				:				str
  __UpperCAmelCase				:				List[str]
  __UpperCAmelCase				:				Optional[List[str]]







@dataclass
class UpperCAmelCase__	:





  """simple docstring"""

  __UpperCAmelCase				:				List[int]
  __UpperCAmelCase				:				List[int]
  __UpperCAmelCase				:				Optional[List[int]]   =					None
  __UpperCAmelCase				:				Optional[List[int]]   =					None







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Dict   =					'''train'''
  __UpperCAmelCase				:				Optional[int]   =					'''dev'''
  __UpperCAmelCase				:				Optional[Any]   =					'''test'''







class UpperCAmelCase__	:





  """simple docstring"""

  @staticmethod
  def   __lowercase		(		_a      :     int							,_a      :     Union[Split, str]       ):


     '''simple docstring'''






     raise NotImplementedError


  @staticmethod
  def   __lowercase		(		_a      :     str       ):


     '''simple docstring'''






     raise NotImplementedError


  @staticmethod
  def   __lowercase		(		_a      :     List[InputExample]							,_a      :     List[str]							,_a      :     int							,_a      :     PreTrainedTokenizer							,_a      :     List[str]=False							,_a      :     Tuple="[CLS]"							,_a      :     Optional[int]=1							,_a      :     List[Any]="[SEP]"							,_a      :     List[str]=False							,_a      :     Optional[int]=False							,_a      :     Optional[Any]=0							,_a      :     List[str]=0							,_a      :     Tuple=-100							,_a      :     Optional[int]=0							,_a      :     int=True							,):


     '''simple docstring'''






     _a				:	Optional[Any]			=   {label: i for i, label in enumerate(_a       )}

     _a				:	str			=   []
     for ex_index, example in enumerate(_a       ):
        if ex_index % 1_0000 == 0:
           logger.info('Writing example %d of %d'							,_a							,len(_a       )       )

        _a				:	List[Any]			=   []
        _a				:	List[str]			=   []
        for word, label in zip(example.words							,example.labels       ):
           _a				:	Dict			=   tokenizer.tokenize(_a       )

           # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
           if len(_a       ) > 0:
              tokens.extend(_a       )
              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_a       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        _a				:	Dict			=   tokenizer.num_special_tokens_to_add()
        if len(_a       ) > max_seq_length - special_tokens_count:
           _a				:	int			=   tokens[: (max_seq_length - special_tokens_count)]
           _a				:	Optional[Any]			=   label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
           # roberta uses an extra separator b/w pairs of sentences
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
        _a				:	Optional[Any]			=   [sequence_a_segment_id] * len(_a       )

        if cls_token_at_end:
           tokens += [cls_token]
           label_ids += [pad_token_label_id]
           segment_ids += [cls_token_segment_id]
        else:
           _a				:	str			=   [cls_token] + tokens
           _a				:	List[Any]			=   [pad_token_label_id] + label_ids
           _a				:	Optional[int]			=   [cls_token_segment_id] + segment_ids

        _a				:	List[Any]			=   tokenizer.convert_tokens_to_ids(_a       )

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        _a				:	Optional[int]			=   [1 if mask_padding_with_zero else 0] * len(_a       )

        # Zero-pad up to the sequence length.
        _a				:	Tuple			=   max_seq_length - len(_a       )
        if pad_on_left:
           _a				:	Dict			=   ([pad_token] * padding_length) + input_ids
           _a				:	List[str]			=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
           _a				:	int			=   ([pad_token_segment_id] * padding_length) + segment_ids
           _a				:	List[Any]			=   ([pad_token_label_id] * padding_length) + label_ids
        else:
           input_ids += [pad_token] * padding_length
           input_mask += [0 if mask_padding_with_zero else 1] * padding_length
           segment_ids += [pad_token_segment_id] * padding_length
           label_ids += [pad_token_label_id] * padding_length

        assert len(_a       ) == max_seq_length
        assert len(_a       ) == max_seq_length
        assert len(_a       ) == max_seq_length
        assert len(_a       ) == max_seq_length

        if ex_index < 5:
           logger.info('*** Example ***'       )
           logger.info('guid: %s'							,example.guid       )
           logger.info('tokens: %s'							,' '.join([str(_a       ) for x in tokens]       )       )
           logger.info('input_ids: %s'							,' '.join([str(_a       ) for x in input_ids]       )       )
           logger.info('input_mask: %s'							,' '.join([str(_a       ) for x in input_mask]       )       )
           logger.info('segment_ids: %s'							,' '.join([str(_a       ) for x in segment_ids]       )       )
           logger.info('label_ids: %s'							,' '.join([str(_a       ) for x in label_ids]       )       )

        if "token_type_ids" not in tokenizer.model_input_names:
           _a				:	Tuple			=   None

        features.append(
            InputFeatures(
                input_ids=_a							,attention_mask=_a							,token_type_ids=_a							,label_ids=_a       )       )
     return features


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset







  class UpperCAmelCase__	( lowercase__	):





    """simple docstring"""

    __UpperCAmelCase				:				List[InputFeatures]
    __UpperCAmelCase				:				int   =					nn.CrossEntropyLoss().ignore_index


    def __init__(		self      :     int							,_a      :     TokenClassificationTask							,_a      :     str							,_a      :     PreTrainedTokenizer							,_a      :     List[str]							,_a      :     str							,_a      :     Optional[int] = None							,_a      :     Any=False							,_a      :     Split = Split.train							,):


       '''simple docstring'''






       _a				:	Optional[int]			=   os.path.join(
           _a							,'cached_{}_{}_{}'.format(mode.value							,tokenizer.__class__.__name__							,str(_a       )       )							,)

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       _a				:	Dict			=   cached_features_file + '.lock'
       with FileLock(_a       ):
          if os.path.exists(_a       ) and not overwrite_cache:
             logger.info(F"""Loading features from cached file {cached_features_file}"""       )
             _a				:	Optional[Any]			=   torch.load(_a       )
          else:
             logger.info(F"""Creating features from dataset file at {data_dir}"""       )
             _a				:	Tuple			=   token_classification_task.read_examples_from_file(_a							,_a       )
             # TODO clean up all this to leverage built-in features of tokenizers
             _a				:	Dict			=   token_classification_task.convert_examples_to_features(
                 _a							,_a							,_a							,_a							,cls_token_at_end=bool(model_type in ['xlnet']       )							,cls_token=tokenizer.cls_token							,cls_token_segment_id=2 if model_type in ['xlnet'] else 0							,sep_token=tokenizer.sep_token							,sep_token_extra=_a							,pad_on_left=bool(tokenizer.padding_side == 'left'       )							,pad_token=tokenizer.pad_token_id							,pad_token_segment_id=tokenizer.pad_token_type_id							,pad_token_label_id=self.pad_token_label_id							,)
             logger.info(F"""Saving features into cached file {cached_features_file}"""       )
             torch.save(self.features							,_a       )


    def __len__(		self      :     List[Any]       ):


       '''simple docstring'''






       return len(self.features       )


    def __getitem__(		self      :     Tuple							,_a      :     Any       ):


       '''simple docstring'''






       return self.features[i]


if is_tf_available():
  import tensorflow as tf







  class UpperCAmelCase__	:





    """simple docstring"""

    __UpperCAmelCase				:				List[InputFeatures]
    __UpperCAmelCase				:				int   =					-100


    def __init__(		self      :     Tuple							,_a      :     TokenClassificationTask							,_a      :     str							,_a      :     PreTrainedTokenizer							,_a      :     List[str]							,_a      :     str							,_a      :     Optional[int] = None							,_a      :     Union[str, Any]=False							,_a      :     Split = Split.train							,):


       '''simple docstring'''






       _a				:	Optional[int]			=   token_classification_task.read_examples_from_file(_a							,_a       )
       # TODO clean up all this to leverage built-in features of tokenizers
       _a				:	str			=   token_classification_task.convert_examples_to_features(
           _a							,_a							,_a							,_a							,cls_token_at_end=bool(model_type in ['xlnet']       )							,cls_token=tokenizer.cls_token							,cls_token_segment_id=2 if model_type in ['xlnet'] else 0							,sep_token=tokenizer.sep_token							,sep_token_extra=_a							,pad_on_left=bool(tokenizer.padding_side == 'left'       )							,pad_token=tokenizer.pad_token_id							,pad_token_segment_id=tokenizer.pad_token_type_id							,pad_token_label_id=self.pad_token_label_id							,)

       def gen():
          for ex in self.features:
             if ex.token_type_ids is None:
                yield (
                    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                    ex.label_ids,
                )
             else:
                yield (
                    {
                        "input_ids": ex.input_ids,
                        "attention_mask": ex.attention_mask,
                        "token_type_ids": ex.token_type_ids,
                    },
                    ex.label_ids,
                )

       if "token_type_ids" not in tokenizer.model_input_names:
          _a				:	Union[str, Any]			=   tf.data.Dataset.from_generator(
              _a							,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)							,(
                  {'input_ids': tf.TensorShape([None]       ), 'attention_mask': tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )							,)
       else:
          _a				:	int			=   tf.data.Dataset.from_generator(
              _a							,({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)							,(
                  {
                      'input_ids': tf.TensorShape([None]       ),
                      'attention_mask': tf.TensorShape([None]       ),
                      'token_type_ids': tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )							,)


    def   __lowercase		(		self      :     int       ):


       '''simple docstring'''






       _a				:	Any			=   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

       return self.dataset


    def __len__(		self      :     Optional[int]       ):


       '''simple docstring'''






       return len(self.features       )


    def __getitem__(		self      :     Any							,_a      :     Any       ):


       '''simple docstring'''






       return self.features[i]





'''simple docstring'''







import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
  import torch

  from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

  if is_vision_available():
    from transformers import MaskaFormerImageProcessor

if is_vision_available():
  from PIL import Image







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     int							,_a      :     Any							,_a      :     Optional[int]=2							,_a      :     Optional[Any]=True							,_a      :     Dict=False							,_a      :     Dict=10							,_a      :     Any=3							,_a      :     str=32 * 8							,_a      :     Optional[int]=32 * 8							,_a      :     int=4							,_a      :     str=64							,):


     '''simple docstring'''






     _a				:	Dict			=   parent
     _a				:	Union[str, Any]			=   batch_size
     _a				:	Tuple			=   is_training
     _a				:	List[str]			=   use_auxiliary_loss
     _a				:	Optional[Any]			=   num_queries
     _a				:	str			=   num_channels
     _a				:	List[str]			=   min_size
     _a				:	int			=   max_size
     _a				:	Optional[int]			=   num_labels
     _a				:	List[str]			=   hidden_dim
     _a				:	int			=   hidden_dim


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Tuple			=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
         _a       )

     _a				:	Optional[Any]			=   torch.ones([self.batch_size, self.min_size, self.max_size]							,device=_a       )

     _a				:	Union[str, Any]			=   (
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,device=_a       ) > 0.5
     ).float()
     _a				:	Tuple			=   (torch.rand((self.batch_size, self.num_labels)							,device=_a       ) > 0.5).long()

     _a				:	Dict			=   self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   MaskaFormerConfig(
         hidden_size=self.hidden_dim							,)
     _a				:	str			=   self.num_queries
     _a				:	Union[str, Any]			=   self.num_labels

     _a				:	Tuple			=   [1, 1, 1, 1]
     _a				:	Dict			=   self.num_channels

     _a				:	str			=   64
     _a				:	Tuple			=   128
     _a				:	Optional[Any]			=   self.hidden_dim
     _a				:	Union[str, Any]			=   self.hidden_dim
     _a				:	List[Any]			=   self.hidden_dim
     return config


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a,  _a,  _a,  _a,  _a				:	Optional[Any]			=   self.prepare_config_and_inputs()
     _a				:	str			=   {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
     return config, inputs_dict


  def   __lowercase		(		self      :     List[str]							,_a      :     Optional[Any]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	str			=   output.encoder_hidden_states
     _a				:	Any			=   output.pixel_decoder_hidden_states
     _a				:	Optional[Any]			=   output.transformer_decoder_hidden_states

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


  def   __lowercase		(		self      :     List[str]							,_a      :     str							,_a      :     List[Any]							,_a      :     Any							,_a      :     Union[str, Any]=False       ):


     '''simple docstring'''






     with torch.no_grad():
        _a				:	str			=   MaskaFormerModel(config=_a       )
        model.to(_a       )
        model.eval()

        _a				:	Any			=   model(pixel_values=_a							,pixel_mask=_a       )
        _a				:	Optional[Any]			=   model(_a							,output_hidden_states=_a       )

     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(_a							,_a       )


  def   __lowercase		(		self      :     Tuple							,_a      :     List[Any]							,_a      :     Union[str, Any]							,_a      :     Tuple							,_a      :     List[str]							,_a      :     Any       ):


     '''simple docstring'''






     _a				:	int			=   MaskaFormerForUniversalSegmentation(config=_a       )
     model.to(_a       )
     model.eval()

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

     with torch.no_grad():
        _a				:	Any			=   model(pixel_values=_a							,pixel_mask=_a       )
        _a				:	Optional[int]			=   model(_a       )

        comm_check_on_output(_a       )

        _a				:	List[str]			=   model(
            pixel_values=_a							,pixel_mask=_a							,mask_labels=_a							,class_labels=_a       )

     comm_check_on_output(_a       )

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







@require_torch
class UpperCAmelCase__	( lowercase__    ,				lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  __UpperCAmelCase				:				Dict   =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  __UpperCAmelCase				:				Dict   =					False
  __UpperCAmelCase				:				Tuple   =					False
  __UpperCAmelCase				:				Dict   =					False
  __UpperCAmelCase				:				List[Any]   =					False


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   MaskaFormerModelTester(self       )
     _a				:	Dict			=   ConfigTester(self							,config_class=_a							,has_text_modality=_a       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     self.config_tester.run_common_tests()


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_a							,**_a							,output_hidden_states=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_a       )


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


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


  @unittest.skip(reason='Mask2Former does not use token embeddings'       )
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a,  _a				:	Any			=   self.model_tester.prepare_config_and_inputs_for_common()

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

        _a				:	List[Any]			=   ['pixel_values']
        self.assertListEqual(arg_names[:1]							,_a       )


  @slow
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
        _a				:	Dict			=   MaskaFormerModel.from_pretrained(_a       )
        self.assertIsNotNone(_a       )


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	int			=   (self.model_tester.min_size,) * 2
     _a				:	Any			=   {
         'pixel_values': torch.randn((2, 3, *size)							,device=_a       ),
         'mask_labels': torch.randn((2, 10, *size)							,device=_a       ),
         'class_labels': torch.zeros(2							,10							,device=_a       ).long(),
     }
     _a				:	List[Any]			=   self.model_tester.get_config()

     _a				:	int			=   MaskaFormerForUniversalSegmentation(_a       ).to(_a       )
     _a				:	str			=   model(**_a       )
     self.assertTrue(outputs.loss is not None       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a,  _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_a							,**_a							,output_hidden_states=_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a,  _a				:	Dict			=   self.model_tester.prepare_config_and_inputs_for_common()

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


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if not self.model_tester.is_training:
        return

     _a				:	List[str]			=   self.all_model_classes[1]
     _a,  _a,  _a,  _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs()

     _a				:	Any			=   model_class(_a       )
     model.to(_a       )
     model.train()

     _a				:	Union[str, Any]			=   model(_a							,mask_labels=_a							,class_labels=_a       ).loss
     loss.backward()


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.all_model_classes[1]
     _a,  _a,  _a,  _a,  _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs()
     _a				:	str			=   True
     _a				:	str			=   True

     _a				:	List[str]			=   model_class(_a       ).to(_a       )
     model.train()

     _a				:	Optional[int]			=   model(_a							,mask_labels=_a							,class_labels=_a       )

     _a				:	Tuple			=   outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     _a				:	str			=   outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

     _a				:	Dict			=   outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

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

     outputs.loss.backward(retain_graph=_a       )

     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         =    1e-4





def 					UpperCAmelCase_		():






   """simple docstring"""

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







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





  """simple docstring"""

  @cached_property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     return "facebook/mask2former-swin-small-coco-instance"


  @cached_property
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[str]			=   MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(_a       )
     _a				:	int			=   self.default_image_processor
     _a				:	Tuple			=   prepare_img()
     _a				:	Any			=   image_processor(_a							,return_tensors='pt'       ).to(_a       )
     _a				:	Union[str, Any]			=   inputs['pixel_values'].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
     # check size
     self.assertEqual(_a							,(1, 3, 384, 384)       )

     with torch.no_grad():
        _a				:	Optional[Any]			=   model(**_a       )

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

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

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


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






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

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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Any			=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_a       ).eval()
     _a				:	Tuple			=   self.default_image_processor

     _a				:	Tuple			=   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				:	str			=   inputs['pixel_values'].to(_a       )
     _a				:	str			=   [el.to(_a       ) for el in inputs['mask_labels']]
     _a				:	Dict			=   [el.to(_a       ) for el in inputs['class_labels']]

     with torch.no_grad():
        _a				:	List[str]			=   model(**_a       )

     self.assertTrue(outputs.loss is not None       )



