

import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image



class        lowercase		:



 '''simple docstring'''


 def __init__(self			,			__a			,			__a=2			,			__a=True			,			__a=False			,			__a=10			,			__a=3			,			__a=32 * 8			,			__a=32 * 8			,			__a=4			,			__a=64			,			)					->   str:






  """simple docstring"""





  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__     							=							hidden_dim
  UpperCAmelCase__     							=							hidden_dim




 def     UpperCamelCase__				(self					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__     							=							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
      lowerCAmelCase_					)

  UpperCAmelCase__     							=							torch.ones([self.batch_size, self.min_size, self.max_size]			,			device=lowerCAmelCase_					)

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

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




 def     UpperCamelCase__				(self					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__     							=							MaskaFormerConfig(
      hidden_size=self.hidden_dim			,			)
  UpperCAmelCase__     							=							self.num_queries
  UpperCAmelCase__     							=							self.num_labels

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

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




 def     UpperCamelCase__				(self					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__     							=							self.prepare_config_and_inputs()
  UpperCAmelCase__     							=							{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
  return config, inputs_dict




 def     UpperCamelCase__				(self			,			__a			,			__a					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__     							=							output.encoder_hidden_states
  UpperCAmelCase__     							=							output.pixel_decoder_hidden_states
  UpperCAmelCase__     							=							output.transformer_decoder_hidden_states

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




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			__a=False					)					->   List[str]:






  """simple docstring"""





  with torch.no_grad():
   UpperCAmelCase__     							=							MaskaFormerModel(config=lowerCAmelCase_					)
   model.to(lowerCAmelCase_					)
   model.eval()

   UpperCAmelCase__     							=							model(pixel_values=lowerCAmelCase_			,			pixel_mask=lowerCAmelCase_					)
   UpperCAmelCase__     							=							model(lowerCAmelCase_			,			output_hidden_states=lowerCAmelCase_					)

  self.parent.assertEqual(
      output.transformer_decoder_last_hidden_state.shape			,			(self.batch_size, self.num_queries, self.hidden_dim)			,			)
  # let's ensure the other two hidden state exists
  self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
  self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

  if output_hidden_states:
   self.check_output_hidden_state(lowerCAmelCase_			,			lowerCAmelCase_					)




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			__a			,			__a					)					->   str:






  """simple docstring"""





  UpperCAmelCase__     							=							MaskaFormerForUniversalSegmentation(config=lowerCAmelCase_					)
  model.to(lowerCAmelCase_					)
  model.eval()

  def comm_check_on_output(__a					):
   # 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=lowerCAmelCase_			,			pixel_mask=lowerCAmelCase_					)
   UpperCAmelCase__     							=							model(lowerCAmelCase_					)

   comm_check_on_output(lowerCAmelCase_					)

   UpperCAmelCase__     							=							model(
       pixel_values=lowerCAmelCase_			,			pixel_mask=lowerCAmelCase_			,			mask_labels=lowerCAmelCase_			,			class_labels=lowerCAmelCase_					)

  comm_check_on_output(lowerCAmelCase_					)

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







@require_torch
class        lowercase		(			_a					,							_a					,							unittest.TestCase   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE   				=							{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE   				=							False
 __SCREAMING_SNAKE_CASE   				=							False
 __SCREAMING_SNAKE_CASE   				=							False
 __SCREAMING_SNAKE_CASE   				=							False




 def     UpperCamelCase__				(self					)					->   List[Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							MaskaFormerModelTester(self					)
  UpperCAmelCase__     							=							ConfigTester(self			,			config_class=lowerCAmelCase_			,			has_text_modality=lowerCAmelCase_					)




 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





  self.config_tester.run_common_tests()




 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase_			,			**lowerCAmelCase_			,			output_hidden_states=lowerCAmelCase_					)




 def     UpperCamelCase__				(self					)					->   Optional[int]:






  """simple docstring"""





  UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase_					)




 @unittest.skip(reason='Mask2Former does not use inputs_embeds'					)
 def     UpperCamelCase__				(self					)					->   str:






  """simple docstring"""





  pass




 @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'					)
 def     UpperCamelCase__				(self					)					->   int:






  """simple docstring"""





  pass




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






  """simple docstring"""





  pass




 @unittest.skip(reason='Mask2Former does not use token embeddings'					)
 def     UpperCamelCase__				(self					)					->   Dict:






  """simple docstring"""





  pass




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






  """simple docstring"""





  pass




 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
 def     UpperCamelCase__				(self					)					->   Optional[int]:






  """simple docstring"""





  pass




 def     UpperCamelCase__				(self					)					->   Tuple:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   UpperCAmelCase__     							=							model_class(lowerCAmelCase_					)
   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]			,			lowerCAmelCase_					)




 @slow
 def     UpperCamelCase__				(self					)					->   List[Any]:






  """simple docstring"""





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




 def     UpperCamelCase__				(self					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__     							=							(self.model_tester.min_size,) * 2
  UpperCAmelCase__     							=							{
      'pixel_values': torch.randn((2, 3, *size)			,			device=lowerCAmelCase_					),
      'mask_labels': torch.randn((2, 10, *size)			,			device=lowerCAmelCase_					),
      'class_labels': torch.zeros(2			,			10			,			device=lowerCAmelCase_					).long(),
  }
  UpperCAmelCase__     							=							self.model_tester.get_config()

  UpperCAmelCase__     							=							MaskaFormerForUniversalSegmentation(lowerCAmelCase_					).to(lowerCAmelCase_					)
  UpperCAmelCase__     							=							model(**lowerCAmelCase_					)
  self.assertTrue(outputs.loss is not None					)




 def     UpperCamelCase__				(self					)					->   Dict:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase_			,			**lowerCAmelCase_			,			output_hidden_states=lowerCAmelCase_					)




 def     UpperCamelCase__				(self					)					->   Dict:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs_for_common()

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




 def     UpperCamelCase__				(self					)					->   str:






  """simple docstring"""





  if not self.model_tester.is_training:
   return

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

  UpperCAmelCase__     							=							model_class(lowerCAmelCase_					)
  model.to(lowerCAmelCase_					)
  model.train()

  UpperCAmelCase__     							=							model(lowerCAmelCase_			,			mask_labels=lowerCAmelCase_			,			class_labels=lowerCAmelCase_					).loss
  loss.backward()




 def     UpperCamelCase__				(self					)					->   int:






  """simple docstring"""





  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(lowerCAmelCase_					).to(lowerCAmelCase_					)
  model.train()

  UpperCAmelCase__     							=							model(lowerCAmelCase_			,			mask_labels=lowerCAmelCase_			,			class_labels=lowerCAmelCase_					)

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

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

  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=lowerCAmelCase_					)

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







_UpperCamelCase     			=     1e-4
def 		UpperCamelCase_(     )			->					Any:
 UpperCAmelCase__     							=							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					)					->   List[Any]:






  """simple docstring"""





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




 @cached_property
 def     UpperCamelCase__				(self					)					->   Dict:






  """simple docstring"""





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




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase_					)
  UpperCAmelCase__     							=							self.default_image_processor
  UpperCAmelCase__     							=							prepare_img()
  UpperCAmelCase__     							=							image_processor(lowerCAmelCase_			,			return_tensors='pt'					).to(lowerCAmelCase_					)
  UpperCAmelCase__     							=							inputs['pixel_values'].shape
  # check size is divisible by 32
  self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
  # check size
  self.assertEqual(lowerCAmelCase_			,			(1, 3, 384, 384)					)

  with torch.no_grad():
   UpperCAmelCase__     							=							model(**lowerCAmelCase_					)

  UpperCAmelCase__     							=							torch.tensor(
      [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]					).to(lowerCAmelCase_					)
  self.assertTrue(
      torch.allclose(
          outputs.encoder_last_hidden_state[0, 0, :3, :3]			,			lowerCAmelCase_			,			atol=lowerCAmelCase_					)					)

  UpperCAmelCase__     							=							torch.tensor(
      [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]					).to(lowerCAmelCase_					)
  self.assertTrue(
      torch.allclose(
          outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,			lowerCAmelCase_			,			atol=lowerCAmelCase_					)					)

  UpperCAmelCase__     							=							torch.tensor(
      [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]					).to(lowerCAmelCase_					)
  self.assertTrue(
      torch.allclose(
          outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,			lowerCAmelCase_			,			atol=lowerCAmelCase_					)					)




 def     UpperCamelCase__				(self					)					->   str:






  """simple docstring"""





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

  with torch.no_grad():
   UpperCAmelCase__     							=							model(**lowerCAmelCase_					)
  # masks_queries_logits
  UpperCAmelCase__     							=							outputs.masks_queries_logits
  self.assertEqual(
      masks_queries_logits.shape			,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					)
  UpperCAmelCase__     							=							[
      [-8.78_39, -9.00_56, -8.81_21],
      [-7.41_04, -7.03_13, -6.54_01],
      [-6.61_05, -6.34_27, -6.46_75],
  ]
  UpperCAmelCase__     							=							torch.tensor(lowerCAmelCase_					).to(lowerCAmelCase_					)
  self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,			lowerCAmelCase_			,			atol=lowerCAmelCase_					)					)
  # class_queries_logits
  UpperCAmelCase__     							=							outputs.class_queries_logits
  self.assertEqual(class_queries_logits.shape			,			(1, model.config.num_queries, model.config.num_labels + 1)					)
  UpperCAmelCase__     							=							torch.tensor(
      [
          [1.83_24, -8.08_35, -4.19_22],
          [0.84_50, -9.00_50, -3.60_53],
          [0.30_45, -7.72_93, -3.02_75],
      ]					).to(lowerCAmelCase_					)
  self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,			lowerCAmelCase_			,			atol=lowerCAmelCase_					)					)




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase_					).eval()
  UpperCAmelCase__     							=							self.default_image_processor

  UpperCAmelCase__     							=							image_processor(
      [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]			,			segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]			,			return_tensors='pt'			,			)

  UpperCAmelCase__     							=							inputs['pixel_values'].to(lowerCAmelCase_					)
  UpperCAmelCase__     							=							[el.to(lowerCAmelCase_					) for el in inputs['mask_labels']]
  UpperCAmelCase__     							=							[el.to(lowerCAmelCase_					) for el in inputs['class_labels']]

  with torch.no_grad():
   UpperCAmelCase__     							=							model(**lowerCAmelCase_					)

  self.assertTrue(outputs.loss is not None					)









'''simple docstring'''


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

