





'''simple docstring'''




import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


__SCREAMING_SNAKE_CASE     :int       =				'''\

'''

__SCREAMING_SNAKE_CASE     :List[Any]       =				'''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

__SCREAMING_SNAKE_CASE     :Tuple       =				'''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''







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




 def      lowercase	(					self       :    int    ):
      return datasets.MetricInfo(
          description=_DESCRIPTION	,			citation=_CITATION	,			inputs_description=_KWARGS_DESCRIPTION	,			features=datasets.Features(
              {
                  "input_texts": datasets.Value("string"    ),
              }    )	,			reference_urls=["https://huggingface.co/docs/transformers/perplexity"]	,			)




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    int	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int = 1_6	,			snake_case_       :    bool = True	,			snake_case_       :    int=None    ):
      if device is not None:
           assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
           if device == "gpu":
                _UpperCAmelCase     							=   "cuda"
      else:
           _UpperCAmelCase     							=   "cuda" if torch.cuda.is_available() else "cpu"

      _UpperCAmelCase     							=   AutoModelForCausalLM.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   model.to(snake_case_    )

      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(snake_case_    )

      # if batch_size > 1 (which generally leads to padding being required), and
      # if there is not an already assigned pad_token, assign an existing
      # special token to also be the padding token
      if tokenizer.pad_token is None and batch_size > 1:
           _UpperCAmelCase     							=   list(tokenizer.special_tokens_map_extended.values()    )
           # check that the model already has at least one special token defined
           assert (
               len(snake_case_    ) > 0
           ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
           # assign one of the special tokens to also be the pad token
           tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}    )

      if add_start_token:
           # leave room for <BOS> token to be added:
           assert (
               tokenizer.bos_token is not None
           ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
           _UpperCAmelCase     							=   model.config.max_length - 1
      else:
           _UpperCAmelCase     							=   model.config.max_length

      _UpperCAmelCase     							=   tokenizer(
          snake_case_	,			add_special_tokens=snake_case_	,			padding=snake_case_	,			truncation=snake_case_	,			max_length=snake_case_	,			return_tensors="pt"	,			return_attention_mask=snake_case_	,			).to(snake_case_    )

      _UpperCAmelCase     							=   encodings["input_ids"]
      _UpperCAmelCase     							=   encodings["attention_mask"]

      # check that each input is long enough:
      if add_start_token:
           assert torch.all(torch.ge(attn_masks.sum(1    )	,			1    )    ), "Each input text must be at least one token long."
      else:
           assert torch.all(
               torch.ge(attn_masks.sum(1    )	,			2    )    ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

      _UpperCAmelCase     							=   []
      _UpperCAmelCase     							=   CrossEntropyLoss(reduction="none"    )

      for start_index in logging.tqdm(range(0	,			len(snake_case_    )	,			snake_case_    )    ):
           _UpperCAmelCase     							=   min(start_index + batch_size	,			len(snake_case_    )    )
           _UpperCAmelCase     							=   encoded_texts[start_index:end_index]
           _UpperCAmelCase     							=   attn_masks[start_index:end_index]

           if add_start_token:
                _UpperCAmelCase     							=   torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0    )    ).to(snake_case_    )
                _UpperCAmelCase     							=   torch.cat([bos_tokens_tensor, encoded_batch]	,			dim=1    )
                _UpperCAmelCase     							=   torch.cat(
                    [torch.ones(bos_tokens_tensor.size()	,			dtype=torch.intaa    ).to(snake_case_    ), attn_mask]	,			dim=1    )

           _UpperCAmelCase     							=   encoded_batch

           with torch.no_grad():
                _UpperCAmelCase     							=   model(snake_case_	,			attention_mask=snake_case_    ).logits

           _UpperCAmelCase     							=   out_logits[..., :-1, :].contiguous()
           _UpperCAmelCase     							=   labels[..., 1:].contiguous()
           _UpperCAmelCase     							=   attn_mask[..., 1:].contiguous()

           _UpperCAmelCase     							=   torch.expa(
               (loss_fct(shift_logits.transpose(1	,			2    )	,			snake_case_    ) * shift_attention_mask_batch).sum(1    )
               / shift_attention_mask_batch.sum(1    )    )

           ppls += perplexity_batch.tolist()

      return {"perplexities": ppls, "mean_perplexity": np.mean(snake_case_    )}











'''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   A_	:




 def __init__(					self       :    str	,			snake_case_       :    int	,			snake_case_       :    Union[str, Any]=2	,			snake_case_       :    List[Any]=True	,			snake_case_       :    str=False	,			snake_case_       :    str=1_0	,			snake_case_       :    str=3	,			snake_case_       :    Dict=3_2 * 4	,			snake_case_       :    Any=3_2 * 6	,			snake_case_       :    Optional[Any]=4	,			snake_case_       :    Optional[int]=3_2	,			):
      _UpperCAmelCase     							=   parent
      _UpperCAmelCase     							=   batch_size
      _UpperCAmelCase     							=   is_training
      _UpperCAmelCase     							=   use_auxiliary_loss
      _UpperCAmelCase     							=   num_queries
      _UpperCAmelCase     							=   num_channels
      _UpperCAmelCase     							=   min_size
      _UpperCAmelCase     							=   max_size
      _UpperCAmelCase     							=   num_labels
      _UpperCAmelCase     							=   mask_feature_size




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
          snake_case_    )

      _UpperCAmelCase     							=   torch.ones([self.batch_size, self.min_size, self.max_size]	,			device=snake_case_    )

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

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




 def      lowercase	(					self       :    List[Any]    ):
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]	,			)	,			decoder_config=DetrConfig(
              decoder_ffn_dim=1_2_8	,			num_queries=self.num_queries	,			decoder_attention_heads=2	,			d_model=self.mask_feature_size	,			)	,			mask_feature_size=self.mask_feature_size	,			fpn_feature_size=self.mask_feature_size	,			num_channels=self.num_channels	,			num_labels=self.num_labels	,			)




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.prepare_config_and_inputs()
      _UpperCAmelCase     							=   {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
      return config, inputs_dict




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Optional[Any]    ):
      _UpperCAmelCase     							=   output.encoder_hidden_states
      _UpperCAmelCase     							=   output.pixel_decoder_hidden_states
      _UpperCAmelCase     							=   output.transformer_decoder_hidden_states

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




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[int]	,			snake_case_       :    Any	,			snake_case_       :    Optional[Any]=False    ):
      with torch.no_grad():
           _UpperCAmelCase     							=   MaskFormerModel(config=snake_case_    )
           model.to(snake_case_    )
           model.eval()

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

      if output_hidden_states:
           self.check_output_hidden_state(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    List[str]	,			snake_case_       :    List[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   MaskFormerForInstanceSegmentation(config=snake_case_    )
      model.to(snake_case_    )
      model.eval()

      def comm_check_on_output(snake_case_       :    int    ):
           # 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():
           _UpperCAmelCase     							=   model(pixel_values=snake_case_	,			pixel_mask=snake_case_    )
           _UpperCAmelCase     							=   model(snake_case_    )

           comm_check_on_output(snake_case_    )

           _UpperCAmelCase     							=   model(
               pixel_values=snake_case_	,			pixel_mask=snake_case_	,			mask_labels=snake_case_	,			class_labels=snake_case_    )

      comm_check_on_output(snake_case_    )

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







@require_torch
class   A_	(  lowerCAmelCase_							,						lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : Dict           =							(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 _lowerCamelCase  : Tuple           =							(
     {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 _lowerCamelCase  : Optional[Any]           =							False
 _lowerCamelCase  : Dict           =							False
 _lowerCamelCase  : Any           =							False
 _lowerCamelCase  : List[Any]           =							False




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   MaskFormerModelTester(self    )
      _UpperCAmelCase     							=   ConfigTester(self	,			config_class=snake_case_	,			has_text_modality=snake_case_    )




 def      lowercase	(					self       :    Optional[Any]    ):
      self.config_tester.run_common_tests()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(snake_case_	,			**snake_case_	,			output_hidden_states=snake_case_    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_    )




 @unittest.skip(reason="MaskFormer does not use inputs_embeds"    )
 def      lowercase	(					self       :    Any    ):
      pass




 @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @unittest.skip(reason="MaskFormer is not a generative model"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @unittest.skip(reason="MaskFormer does not use token embeddings"    )
 def      lowercase	(					self       :    List[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      lowercase	(					self       :    Any    ):
      pass




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def      lowercase	(					self       :    Union[str, Any]    ):
      pass




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

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

           _UpperCAmelCase     							=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    Optional[int]    ):
      for model_name in ["facebook/maskformer-swin-small-coco"]:
           _UpperCAmelCase     							=   MaskFormerModel.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   (self.model_tester.min_size,) * 2
      _UpperCAmelCase     							=   {
          "pixel_values": torch.randn((2, 3, *size)	,			device=snake_case_    ),
          "mask_labels": torch.randn((2, 1_0, *size)	,			device=snake_case_    ),
          "class_labels": torch.zeros(2	,			1_0	,			device=snake_case_    ).long(),
      }

      _UpperCAmelCase     							=   MaskFormerForInstanceSegmentation(MaskFormerConfig()    ).to(snake_case_    )
      _UpperCAmelCase     							=   model(**snake_case_    )
      self.assertTrue(outputs.loss is not None    )




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(snake_case_	,			**snake_case_	,			output_hidden_states=snake_case_    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

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




 def      lowercase	(					self       :    int    ):
      if not self.model_tester.is_training:
           return
      # only MaskFormerForInstanceSegmentation has the loss
      _UpperCAmelCase     							=   self.all_model_classes[1]
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()

      _UpperCAmelCase     							=   model_class(snake_case_    )
      model.to(snake_case_    )
      model.train()

      _UpperCAmelCase     							=   model(snake_case_	,			mask_labels=snake_case_	,			class_labels=snake_case_    ).loss
      loss.backward()




 def      lowercase	(					self       :    int    ):
      # only MaskFormerForInstanceSegmentation has the loss
      _UpperCAmelCase     							=   self.all_model_classes[1]
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      _UpperCAmelCase     							=   True
      _UpperCAmelCase     							=   True

      _UpperCAmelCase     							=   model_class(snake_case_    )
      model.to(snake_case_    )
      model.train()

      _UpperCAmelCase     							=   model(snake_case_	,			mask_labels=snake_case_	,			class_labels=snake_case_    )

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

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

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

      outputs.loss.backward(retain_graph=snake_case_    )

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





__SCREAMING_SNAKE_CASE     :Dict       =				1e-4
def 	UpperCAmelCase_							(					)		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
     return image







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




 @cached_property
 def      lowercase	(					self       :    Dict    ):
      return (
          MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"    )
          if is_vision_available()
          else None
      )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"    ).to(snake_case_    )
      _UpperCAmelCase     							=   self.default_image_processor
      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="pt"    ).to(snake_case_    )
      _UpperCAmelCase     							=   inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )

      _UpperCAmelCase     							=   torch.tensor(
          [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]    ).to(snake_case_    )
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )

      _UpperCAmelCase     							=   torch.tensor(
          [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]    ).to(snake_case_    )
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )

      _UpperCAmelCase     							=   torch.tensor(
          [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]    ).to(snake_case_    )
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor
      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="pt"    ).to(snake_case_    )
      _UpperCAmelCase     							=   inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )
      # masks_queries_logits
      _UpperCAmelCase     							=   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)	,			)
      _UpperCAmelCase     							=   [
          [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
          [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
          [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
      ]
      _UpperCAmelCase     							=   torch.tensor(snake_case_    ).to(snake_case_    )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )
      # class_queries_logits
      _UpperCAmelCase     							=   outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
      _UpperCAmelCase     							=   torch.tensor(
          [
              [1.6_512e00, -5.2_572e00, -3.3_519e00],
              [3.6_169e-02, -5.9_025e00, -2.9_313e00],
              [1.0_766e-04, -7.7_630e00, -5.1_263e00],
          ]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor
      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="pt"    ).to(snake_case_    )
      _UpperCAmelCase     							=   inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )
      # masks_queries_logits
      _UpperCAmelCase     							=   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)	,			)
      _UpperCAmelCase     							=   [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
      _UpperCAmelCase     							=   torch.tensor(snake_case_    ).to(snake_case_    )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )
      # class_queries_logits
      _UpperCAmelCase     							=   outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
      _UpperCAmelCase     							=   torch.tensor(
          [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor

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

      _UpperCAmelCase     							=   inputs["pixel_values"].to(snake_case_    )
      _UpperCAmelCase     							=   [el.to(snake_case_    ) for el in inputs["mask_labels"]]
      _UpperCAmelCase     							=   [el.to(snake_case_    ) for el in inputs["class_labels"]]

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )

      self.assertTrue(outputs.loss is not None    )




