'''simple docstring'''






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCAmelCase    :     Any         =       '''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

_UpperCAmelCase    :     str         =       '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

_UpperCAmelCase    :     Union[str, Any]         =       '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,    _KWARGS_DESCRIPTION			)
class lowercase_       (		datasets.Metric			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			MetricInfo:
        return datasets.MetricInfo(
            description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
                {
                    'predictions': datasets.Sequence(datasets.Value('string', id='token'     ), id='sequence'     ),
                    'references': datasets.Sequence(
                        datasets.Sequence(datasets.Value('string', id='token'     ), id='sequence'     ), id='references'     ),
                }     ), )

   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			List[List[List[str]]], UpperCamelCase__      :			List[List[str]], UpperCamelCase__      :			int = 1, UpperCamelCase__      :			int = 4, )  ->			Dict[str, float]:
        return {
            "google_bleu": gleu_score.corpus_gleu(
                list_of_references=UpperCamelCase__, hypotheses=UpperCamelCase__, min_len=UpperCamelCase__, max_len=UpperCamelCase__     )
        }




'''simple docstring'''






import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
   import torch

   from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

   if is_vision_available():
      from transformers import MaskFormerImageProcessor

if is_vision_available():
   from PIL import Image







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Any=2, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Optional[Any]=False, UpperCamelCase__      :			int=10, UpperCamelCase__      :			List[str]=3, UpperCamelCase__      :			List[str]=32 * 4, UpperCamelCase__      :			List[Any]=32 * 6, UpperCamelCase__      :			Dict=4, UpperCamelCase__      :			str=32, )  ->			Dict:
        _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    		=  mask_feature_size
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Tuple:
        _A    		=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]     ).to(
            UpperCamelCase__     )

        _A    		=  torch.ones([self.batch_size, self.min_size, self.max_size], device=UpperCamelCase__     )

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

        _A    		=  self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        return MaskFormerConfig.from_backbone_and_decoder_configs(
            backbone_config=SwinConfig(
                depths=[1, 1, 1, 1], ), decoder_config=DetrConfig(
                decoder_ffn_dim=1_28, num_queries=self.num_queries, decoder_attention_heads=2, d_model=self.mask_feature_size, ), mask_feature_size=self.mask_feature_size, fpn_feature_size=self.mask_feature_size, num_channels=self.num_channels, num_labels=self.num_labels, )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Tuple:
        _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      :			Optional[int], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict     )  ->			List[Any]:
        _A    		=  output.encoder_hidden_states
        _A    		=  output.pixel_decoder_hidden_states
        _A    		=  output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(UpperCamelCase__     ), len(config.backbone_config.depths     )     )
        self.parent.assertTrue(len(UpperCamelCase__     ), len(config.backbone_config.depths     )     )
        self.parent.assertTrue(len(UpperCamelCase__     ), config.decoder_config.decoder_layers     )
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Optional[Any]=False     )  ->			Optional[Any]:
        with torch.no_grad():
             _A    		=  MaskFormerModel(config=UpperCamelCase__     )
             model.to(UpperCamelCase__     )
             model.eval()

             _A    		=  model(pixel_values=UpperCamelCase__, pixel_mask=UpperCamelCase__     )
             _A    		=  model(UpperCamelCase__, output_hidden_states=UpperCamelCase__     )
        # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
        # encoder and pixel decoder
        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape, (self.batch_size, self.num_queries, self.mask_feature_size), )
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None     )
        self.parent.assertTrue(output.encoder_last_hidden_state is not None     )

        if output_hidden_states:
             self.check_output_hidden_state(UpperCamelCase__, UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Tuple     )  ->			str:
        _A    		=  MaskFormerForInstanceSegmentation(config=UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()

        def comm_check_on_output(UpperCamelCase__      :			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    		=  model(pixel_values=UpperCamelCase__, pixel_mask=UpperCamelCase__     )
             _A    		=  model(UpperCamelCase__     )

             comm_check_on_output(UpperCamelCase__     )

             _A    		=  model(
                 pixel_values=UpperCamelCase__, pixel_mask=UpperCamelCase__, mask_labels=UpperCamelCase__, class_labels=UpperCamelCase__     )

        comm_check_on_output(UpperCamelCase__     )

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







@require_torch
class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

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

   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        _A    		=  MaskFormerModelTester(self     )
        _A    		=  ConfigTester(self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			int:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(UpperCamelCase__, **UpperCamelCase__, output_hidden_states=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCamelCase__     )
   @unittest.skip(reason='MaskFormer does not use inputs_embeds'     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        pass
   @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Union[str, Any]:
        pass
   @unittest.skip(reason='MaskFormer is not a generative model'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        pass
   @unittest.skip(reason='MaskFormer does not use token embeddings'     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Union[str, Any]:
        pass
   @require_torch_multi_gpu
   @unittest.skip(
       reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Union[str, Any]:
        pass
   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        pass
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[str]:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     )
             _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], UpperCamelCase__     )
   @slow
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[int]:
        for model_name in ["facebook/maskformer-swin-small-coco"]:
             _A    		=  MaskFormerModel.from_pretrained(UpperCamelCase__     )
             self.assertIsNotNone(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[Any]:
        _A    		=  (self.model_tester.min_size,) * 2
        _A    		=  {
            'pixel_values': torch.randn((2, 3, *size), device=UpperCamelCase__     ),
            'mask_labels': torch.randn((2, 10, *size), device=UpperCamelCase__     ),
            'class_labels': torch.zeros(2, 10, device=UpperCamelCase__     ).long(),
        }

        _A    		=  MaskFormerForInstanceSegmentation(MaskFormerConfig()     ).to(UpperCamelCase__     )
        _A    		=  model(**UpperCamelCase__     )
        self.assertTrue(outputs.loss is not None     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(UpperCamelCase__, **UpperCamelCase__, output_hidden_states=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Tuple:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     ).to(UpperCamelCase__     )
             _A    		=  model(**UpperCamelCase__, output_attentions=UpperCamelCase__     )
             self.assertTrue(outputs.attentions is not None     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[Any]:
        if not self.model_tester.is_training:
             return
        # only MaskFormerForInstanceSegmentation has the loss
        _A    		=  self.all_model_classes[1]
        _A		,       _A		,       _A		,       _A		,       _A    		=  self.model_tester.prepare_config_and_inputs()

        _A    		=  model_class(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.train()

        _A    		=  model(UpperCamelCase__, mask_labels=UpperCamelCase__, class_labels=UpperCamelCase__     ).loss
        loss.backward()

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[str]:
        # only MaskFormerForInstanceSegmentation has the loss
        _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(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.train()

        _A    		=  model(UpperCamelCase__, mask_labels=UpperCamelCase__, class_labels=UpperCamelCase__     )

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

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

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






_UpperCAmelCase    :     Any         =       1E-4





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image







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







   """simple docstring"""

   @cached_property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        return (
            MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'     )
            if is_vision_available()
            else None
        )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[str]:
        _A    		=  MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'     ).to(UpperCamelCase__     )
        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )
        _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(UpperCamelCase__, (1, 3, 8_00, 10_88)     )

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )

        _A    		=  torch.tensor(
            [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]     ).to(UpperCamelCase__     )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )

        _A    		=  torch.tensor(
            [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]     ).to(UpperCamelCase__     )
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )

        _A    		=  torch.tensor(
            [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]     ).to(UpperCamelCase__     )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[int]:
        _A    		=  (
            MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'     )
            .to(UpperCamelCase__     )
            .eval()
        )
        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )
        _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(UpperCamelCase__, (1, 3, 8_00, 10_88)     )

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )
        # masks_queries_logits
        _A    		=  outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4), )
        _A    		=  [
            [-1.3_737_124, -1.7_724_937, -1.9_364_233],
            [-1.5_977_281, -1.9_867_939, -2.1_523_695],
            [-1.5_795_398, -1.9_269_832, -2.093_942],
        ]
        _A    		=  torch.tensor(UpperCamelCase__     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
        # class_queries_logits
        _A    		=  outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)     )
        _A    		=  torch.tensor(
            [
                [1.6512e00, -5.2572e00, -3.3519e00],
                [3.6169e-02, -5.9025e00, -2.9313e00],
                [1.0766e-04, -7.7630e00, -5.1263e00],
            ]     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  (
            MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'     )
            .to(UpperCamelCase__     )
            .eval()
        )
        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )
        _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(UpperCamelCase__, (1, 3, 8_00, 10_88)     )

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )
        # masks_queries_logits
        _A    		=  outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4), )
        _A    		=  [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
        _A    		=  torch.tensor(UpperCamelCase__     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )
        # class_queries_logits
        _A    		=  outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)     )
        _A    		=  torch.tensor(
            [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]     ).to(UpperCamelCase__     )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], UpperCamelCase__, atol=UpperCamelCase__     )     )

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        _A    		=  (
            MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'     )
            .to(UpperCamelCase__     )
            .eval()
        )
        _A    		=  self.default_image_processor

        _A    		=  image_processor(
            [np.zeros((3, 8_00, 13_33)     ), np.zeros((3, 8_00, 13_33)     )], segmentation_maps=[np.zeros((3_84, 3_84)     ).astype(np.floataa     ), np.zeros((3_84, 3_84)     ).astype(np.floataa     )], return_tensors='pt', )

        _A    		=  inputs['pixel_values'].to(UpperCamelCase__     )
        _A    		=  [el.to(UpperCamelCase__     ) for el in inputs['mask_labels']]
        _A    		=  [el.to(UpperCamelCase__     ) for el in inputs['class_labels']]

        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )

        self.assertTrue(outputs.loss is not None     )



