


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 			SCREAMING_SNAKE_CASE__  :
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	List[str]=2					,							SCREAMING_SNAKE_CASE__    :	int=True					,							SCREAMING_SNAKE_CASE__    :	List[Any]=False					,							SCREAMING_SNAKE_CASE__    :	Any=1_0					,							SCREAMING_SNAKE_CASE__    :	Tuple=3					,							SCREAMING_SNAKE_CASE__    :	str=3_2 * 4					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=3_2 * 6					,							SCREAMING_SNAKE_CASE__    :	Tuple=4					,							SCREAMING_SNAKE_CASE__    :	List[Any]=3_2					,							)		->      Tuple:
             a_ :		List[str]							=  parent
             a_ :		Tuple							=  batch_size
             a_ :		List[str]							=  is_training
             a_ :		Union[str, Any]							=  use_auxiliary_loss
             a_ :		Tuple							=  num_queries
             a_ :		Dict							=  num_channels
             a_ :		List[Any]							=  min_size
             a_ :		Tuple							=  max_size
             a_ :		List[Any]							=  num_labels
             a_ :		List[Any]							=  mask_feature_size
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[str]:
             a_ :		Tuple							=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
                 SCREAMING_SNAKE_CASE__					)

             a_ :		Dict							=  torch.ones([self.batch_size, self.min_size, self.max_size]					,							device=SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  (
                 torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,							device=SCREAMING_SNAKE_CASE__					) > 0.5
             ).float()
             a_ :		List[str]							=  (torch.rand((self.batch_size, self.num_labels)					,							device=SCREAMING_SNAKE_CASE__					) > 0.5).long()

             a_ :		Optional[Any]							=  self.get_config()
             return config, pixel_values, pixel_mask, mask_labels, class_labels
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[str]:
             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 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[Any]:
             a_							,		a_							,		a_							,		a_							,		a_ :		Optional[Any]							=  self.prepare_config_and_inputs()
             a_ :		Optional[Any]							=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
             return config, inputs_dict
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	int					)		->      List[str]:
             a_ :		Tuple							=  output.encoder_hidden_states
             a_ :		Optional[Any]							=  output.pixel_decoder_hidden_states
             a_ :		Union[str, Any]							=  output.transformer_decoder_hidden_states

             self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__					)					,							len(config.backbone_config.depths					)					)
             self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__					)					,							len(config.backbone_config.depths					)					)
             self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__					)					,							config.decoder_config.decoder_layers					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=False					)		->      List[str]:
             with torch.no_grad():
                    a_ :		Dict							=  MaskFormerModel(config=SCREAMING_SNAKE_CASE__					)
                    model.to(SCREAMING_SNAKE_CASE__					)
                    model.eval()

                    a_ :		int							=  model(pixel_values=SCREAMING_SNAKE_CASE__					,							pixel_mask=SCREAMING_SNAKE_CASE__					)
                    a_ :		List[str]							=  model(SCREAMING_SNAKE_CASE__					,							output_hidden_states=SCREAMING_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(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Any:
             a_ :		List[str]							=  MaskFormerForInstanceSegmentation(config=SCREAMING_SNAKE_CASE__					)
             model.to(SCREAMING_SNAKE_CASE__					)
             model.eval()

             def comm_check_on_output(SCREAMING_SNAKE_CASE__    :	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_ :		str							=  model(pixel_values=SCREAMING_SNAKE_CASE__					,							pixel_mask=SCREAMING_SNAKE_CASE__					)
                    a_ :		Any							=  model(SCREAMING_SNAKE_CASE__					)

                    comm_check_on_output(SCREAMING_SNAKE_CASE__					)

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

             comm_check_on_output(SCREAMING_SNAKE_CASE__					)

             self.parent.assertTrue(result.loss is not None					)
             self.parent.assertEqual(result.loss.shape					,							torch.Size([1]					)					)
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Tuple					       =						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
      snake_case__    :					Optional[Any]					       =						(
          {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
          if is_torch_available()
          else {}
      )

      snake_case__    :					Union[str, Any]					       =						False
      snake_case__    :					Dict					       =						False
      snake_case__    :					List[str]					       =						False
      snake_case__    :					Optional[int]					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Dict:
             a_ :		List[Any]							=  MaskFormerModelTester(self					)
             a_ :		Optional[Any]							=  ConfigTester(self					,							config_class=SCREAMING_SNAKE_CASE__					,							has_text_modality=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[str]:
             self.config_tester.run_common_tests()
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[int]:
             a_							,		a_ :		int							=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskformer_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					,							output_hidden_states=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Dict:
             a_ :		Optional[int]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__					)
      @unittest.skip(reason='MaskFormer does not use inputs_embeds'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Dict:
             pass
      @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Any:
             pass
      @unittest.skip(reason='MaskFormer is not a generative model'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Any:
             pass
      @unittest.skip(reason='MaskFormer does not use token embeddings'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      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 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:
             pass
      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             pass
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[Any]:
             a_							,		a_ :		Dict							=  self.model_tester.prepare_config_and_inputs_for_common()

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

                    a_ :		Optional[int]							=  ['pixel_values']
                    self.assertListEqual(arg_names[:1]					,							SCREAMING_SNAKE_CASE__					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Tuple:
             for model_name in ["facebook/maskformer-swin-small-coco"]:
                    a_ :		str							=  MaskFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[int]:
             a_ :		List[str]							=  (self.model_tester.min_size,) * 2
             a_ :		List[Any]							=  {
                 'pixel_values': torch.randn((2, 3, *size)					,							device=SCREAMING_SNAKE_CASE__					),
                 'mask_labels': torch.randn((2, 1_0, *size)					,							device=SCREAMING_SNAKE_CASE__					),
                 'class_labels': torch.zeros(2					,							1_0					,							device=SCREAMING_SNAKE_CASE__					).long(),
             }

             a_ :		Optional[Any]							=  MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  model(**SCREAMING_SNAKE_CASE__					)
             self.assertTrue(outputs.loss is not None					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[Any]:
             a_							,		a_ :		int							=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskformer_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					,							output_hidden_states=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             a_							,		a_ :		int							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_ :		Dict							=  model_class(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
                    a_ :		Any							=  model(**SCREAMING_SNAKE_CASE__					,							output_attentions=SCREAMING_SNAKE_CASE__					)
                    self.assertTrue(outputs.attentions is not None					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Dict:
             if not self.model_tester.is_training:
                    return
             # only MaskFormerForInstanceSegmentation has the loss
             a_ :		Optional[Any]							=  self.all_model_classes[1]
             a_							,		a_							,		a_							,		a_							,		a_ :		Optional[int]							=  self.model_tester.prepare_config_and_inputs()

             a_ :		Optional[Any]							=  model_class(SCREAMING_SNAKE_CASE__					)
             model.to(SCREAMING_SNAKE_CASE__					)
             model.train()

             a_ :		Optional[int]							=  model(SCREAMING_SNAKE_CASE__					,							mask_labels=SCREAMING_SNAKE_CASE__					,							class_labels=SCREAMING_SNAKE_CASE__					).loss
             loss.backward()

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Dict:
             # only MaskFormerForInstanceSegmentation has the loss
             a_ :		Optional[Any]							=  self.all_model_classes[1]
             a_							,		a_							,		a_							,		a_							,		a_ :		Tuple							=  self.model_tester.prepare_config_and_inputs()
             a_ :		Optional[int]							=  True
             a_ :		int							=  True

             a_ :		Any							=  model_class(SCREAMING_SNAKE_CASE__					)
             model.to(SCREAMING_SNAKE_CASE__					)
             model.train()

             a_ :		Dict							=  model(SCREAMING_SNAKE_CASE__					,							mask_labels=SCREAMING_SNAKE_CASE__					,							class_labels=SCREAMING_SNAKE_CASE__					)

             a_ :		Union[str, Any]							=  outputs.encoder_hidden_states[0]
             encoder_hidden_states.retain_grad()

             a_ :		Tuple							=  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_ :		Union[str, Any]							=  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=SCREAMING_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					)
UpperCAmelCase_				:			Optional[int]   =	1e-4







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Dict:

       """simple docstring"""

       a_ :		List[str]							=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
       return image
@require_vision
@slow
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @cached_property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Union[str, Any]:
             return (
                 MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'					)
                 if is_vision_available()
                 else None
             )
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      int:
             a_ :		Union[str, Any]							=  MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[Any]							=  self.default_image_processor
             a_ :		Optional[int]							=  prepare_img()
             a_ :		List[str]							=  image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors='pt'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[int]							=  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(SCREAMING_SNAKE_CASE__					,							(1, 3, 8_0_0, 1_0_8_8)					)

             with torch.no_grad():
                    a_ :		List[Any]							=  model(**SCREAMING_SNAKE_CASE__					)

             a_ :		Dict							=  torch.tensor(
                 [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(
                 torch.allclose(
                     outputs.encoder_last_hidden_state[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)

             a_ :		Optional[Any]							=  torch.tensor(
                 [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(
                 torch.allclose(
                     outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)

             a_ :		Tuple							=  torch.tensor(
                 [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(
                 torch.allclose(
                     outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[str]:
             a_ :		Dict							=  (
                 MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'					)
                 .to(SCREAMING_SNAKE_CASE__					)
                 .eval()
             )
             a_ :		Optional[int]							=  self.default_image_processor
             a_ :		Any							=  prepare_img()
             a_ :		List[str]							=  image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors='pt'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[Any]							=  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(SCREAMING_SNAKE_CASE__					,							(1, 3, 8_0_0, 1_0_8_8)					)

             with torch.no_grad():
                    a_ :		List[Any]							=  model(**SCREAMING_SNAKE_CASE__					)
             # masks_queries_logits
             a_ :		Union[str, Any]							=  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_ :		int							=  [
                 [-1.3737124, -1.7724937, -1.9364233],
                 [-1.5977281, -1.9867939, -2.1523695],
                 [-1.5795398, -1.9269832, -2.093942],
             ]
             a_ :		Optional[Any]							=  torch.tensor(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)
             # class_queries_logits
             a_ :		Optional[Any]							=  outputs.class_queries_logits
             self.assertEqual(
                 class_queries_logits.shape					,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
             a_ :		str							=  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(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Any:
             a_ :		str							=  (
                 MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'					)
                 .to(SCREAMING_SNAKE_CASE__					)
                 .eval()
             )
             a_ :		List[Any]							=  self.default_image_processor
             a_ :		Dict							=  prepare_img()
             a_ :		Any							=  image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors='pt'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  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(SCREAMING_SNAKE_CASE__					,							(1, 3, 8_0_0, 1_0_8_8)					)

             with torch.no_grad():
                    a_ :		int							=  model(**SCREAMING_SNAKE_CASE__					)
             # masks_queries_logits
             a_ :		List[str]							=  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_ :		Tuple							=  [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
             a_ :		Optional[Any]							=  torch.tensor(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)
             # class_queries_logits
             a_ :		Union[str, Any]							=  outputs.class_queries_logits
             self.assertEqual(
                 class_queries_logits.shape					,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
             a_ :		Any							=  torch.tensor(
                 [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Union[str, Any]:
             a_ :		int							=  (
                 MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'					)
                 .to(SCREAMING_SNAKE_CASE__					)
                 .eval()
             )
             a_ :		List[str]							=  self.default_image_processor

             a_ :		Union[str, Any]							=  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'					,							)

             a_ :		List[str]							=  inputs['pixel_values'].to(SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  [el.to(SCREAMING_SNAKE_CASE__					) for el in inputs['mask_labels']]
             a_ :		int							=  [el.to(SCREAMING_SNAKE_CASE__					) for el in inputs['class_labels']]

             with torch.no_grad():
                    a_ :		int							=  model(**SCREAMING_SNAKE_CASE__					)

             self.assertTrue(outputs.loss is not None					)






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Any    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		str							=  filter(lambda __A    : p.requires_grad  ,   model.parameters()    )
       a_ :		List[Any]							=  sum([np.prod(p.size()    ) for p in model_parameters]    )
       return params


UpperCAmelCase_				:			Dict   =	logging.getLogger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		List[str]:

       """simple docstring"""

       if metric == "rouge2":
              a_ :		Dict							=  '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              a_ :		Dict							=  '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              a_ :		Union[str, Any]							=  '{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              a_ :		str							=  '{val_avg_loss:.4f}-{step_count}'
       else:
              raise NotImplementedError(
                  F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
                  ' function.'    )

       a_ :		Dict							=  ModelCheckpoint(
           dirpath=__A  ,   filename=__A  ,   monitor=F"""val_{metric}"""  ,   mode='max'  ,   save_top_k=1  ,   every_n_epochs=1  ,   )

       return checkpoint_callback







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      List[Any]    )     ->		int:

       """simple docstring"""

       return EarlyStopping(
           monitor=F"""val_{metric}"""  ,   mode='min' if 'loss' in metric else 'max'  ,   patience=__A  ,   verbose=__A  ,   )
class 			SCREAMING_SNAKE_CASE__  (    pl.Callback      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Any:
             a_ :		int							=  {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
             pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					)		->      None:
             logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""					)
             a_ :		List[str]							=  trainer.callback_metrics
             trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}					)
             # Log results
             a_ :		Optional[int]							=  Path(pl_module.hparams.output_dir					)
             if type_path == "test":
                    a_ :		Tuple							=  od / 'test_results.txt'
                    a_ :		Optional[int]							=  od / 'test_generations.txt'
             else:
                    # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
                    # If people want this it will be easy enough to add back.
                    a_ :		str							=  od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
                    a_ :		Optional[Any]							=  od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                    results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
                    generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
             with open(SCREAMING_SNAKE_CASE__					,							'a+'					) as writer:
                    for key in sorted(SCREAMING_SNAKE_CASE__					):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           a_ :		int							=  metrics[key]
                           if isinstance(SCREAMING_SNAKE_CASE__					,							torch.Tensor					):
                                  a_ :		List[str]							=  val.item()
                           a_ :		int							=  F"""{key}: {val:.6f}\n"""
                           writer.write(SCREAMING_SNAKE_CASE__					)

             if not save_generations:
                    return

             if "preds" in metrics:
                    a_ :		Optional[Any]							=  '\n'.join(metrics['preds']					)
                    generations_file.open('w+'					).write(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Dict:
             try:
                    a_ :		Any							=  pl_module.model.model.num_parameters()
             except AttributeError:
                    a_ :		List[str]							=  pl_module.model.num_parameters()

             a_ :		Any							=  count_trainable_parameters(SCREAMING_SNAKE_CASE__					)
             # mp stands for million parameters
             trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					)		->      Optional[int]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             return self._write_logs(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							'test'					)

      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      Optional[Any]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             # Uncommenting this will save val generations
             # return self._write_logs(trainer, pl_module, "valid")


