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    A_ :

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

		def 		_UpperCAmelCase   (		self :      Tuple       ):
			__a       =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
			    __SCREAMING_SNAKE_CASE       )

			__a       =  torch.ones([self.batch_size, self.min_size, self.max_size]     ,							device=__SCREAMING_SNAKE_CASE       )

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

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

		def 		_UpperCAmelCase   (		self :      str       ):
			__a       =  MaskaFormerConfig(
			    hidden_size=self.hidden_dim     ,							)
			__a       =  self.num_queries
			__a       =  self.num_labels

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

			__a       =  64
			__a       =  1_28
			__a       =  self.hidden_dim
			__a       =  self.hidden_dim
			__a       =  self.hidden_dim
			return config

		def 		_UpperCAmelCase   (		self :      int       ):
			__a       =  self.prepare_config_and_inputs()
			__a       =  {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
			return config, inputs_dict

		def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
			__a       =  output.encoder_hidden_states
			__a       =  output.pixel_decoder_hidden_states
			__a       =  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_layers       )

		def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any=False       ):
			with torch.no_grad():
				__a       =  MaskaFormerModel(config=__SCREAMING_SNAKE_CASE       )
				model.to(__SCREAMING_SNAKE_CASE       )
				model.eval()

				__a       =  model(pixel_values=__SCREAMING_SNAKE_CASE     ,							pixel_mask=__SCREAMING_SNAKE_CASE       )
				__a       =  model(__SCREAMING_SNAKE_CASE     ,							output_hidden_states=__SCREAMING_SNAKE_CASE       )

			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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
			__a       =  MaskaFormerForUniversalSegmentation(config=__SCREAMING_SNAKE_CASE       )
			model.to(__SCREAMING_SNAKE_CASE       )
			model.eval()

			def comm_check_on_output(__SCREAMING_SNAKE_CASE :      Optional[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=__SCREAMING_SNAKE_CASE     ,							pixel_mask=__SCREAMING_SNAKE_CASE       )
				__a       =  model(__SCREAMING_SNAKE_CASE       )

				comm_check_on_output(__SCREAMING_SNAKE_CASE       )

				__a       =  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    A_ (       _UpperCamelCase			,       _UpperCamelCase			,       unittest.TestCase ):
		_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 :      Optional[int]       ):
			__a       =  MaskaFormerModelTester(self       )
			__a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							has_text_modality=__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      int       ):
			self.config_tester.run_common_tests()

		def 		_UpperCAmelCase   (		self :      int       ):
			__a       =  self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.create_and_check_maskaformer_model(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							output_hidden_states=__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      int       ):
			__a       =  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__SCREAMING_SNAKE_CASE       )

		@unittest.skip(reason="Mask2Former does not use inputs_embeds"       )
		def 		_UpperCAmelCase   (		self :      List[Any]       ):
			pass

		@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"       )
		def 		_UpperCAmelCase   (		self :      List[str]       ):
			pass

		@unittest.skip(reason="Mask2Former is not a generative model"       )
		def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
			pass

		@unittest.skip(reason="Mask2Former does not use token embeddings"       )
		def 		_UpperCAmelCase   (		self :      Optional[int]       ):
			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       ):
			pass

		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
		def 		_UpperCAmelCase   (		self :      Dict       ):
			pass

		def 		_UpperCAmelCase   (		self :      int       ):
			__a       =  self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__a       =  model_class(__SCREAMING_SNAKE_CASE       )
				__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]     ,							__SCREAMING_SNAKE_CASE       )

		@slow
		def 		_UpperCAmelCase   (		self :      Any       ):
			for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
				__a       =  MaskaFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
				self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      List[str]       ):
			__a       =  (self.model_tester.min_size,) * 2
			__a       =  {
			    "pixel_values": torch.randn((2, 3, *size)     ,							device=__SCREAMING_SNAKE_CASE       ),
			    "mask_labels": torch.randn((2, 10, *size)     ,							device=__SCREAMING_SNAKE_CASE       ),
			    "class_labels": torch.zeros(2     ,							10     ,							device=__SCREAMING_SNAKE_CASE       ).long(),
			}
			__a       =  self.model_tester.get_config()

			__a       =  MaskaFormerForUniversalSegmentation(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )
			__a       =  model(**__SCREAMING_SNAKE_CASE       )
			self.assertTrue(outputs.loss is not None       )

		def 		_UpperCAmelCase   (		self :      int       ):
			__a       =  self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.create_and_check_maskaformer_model(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							output_hidden_states=__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
			__a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

		def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
			if not self.model_tester.is_training:
				return

			__a       =  self.all_model_classes[1]
			__a       =  self.model_tester.prepare_config_and_inputs()

			__a       =  model_class(__SCREAMING_SNAKE_CASE       )
			model.to(__SCREAMING_SNAKE_CASE       )
			model.train()

			__a       =  model(__SCREAMING_SNAKE_CASE     ,							mask_labels=__SCREAMING_SNAKE_CASE     ,							class_labels=__SCREAMING_SNAKE_CASE       ).loss
			loss.backward()

		def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
			__a       =  self.all_model_classes[1]
			__a       =  self.model_tester.prepare_config_and_inputs()
			__a       =  True
			__a       =  True

			__a       =  model_class(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )
			model.train()

			__a       =  model(__SCREAMING_SNAKE_CASE     ,							mask_labels=__SCREAMING_SNAKE_CASE     ,							class_labels=__SCREAMING_SNAKE_CASE       )

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

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

			__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=__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       )
SCREAMING_SNAKE_CASE :	Optional[int]							     =		1e-4



def   __A		(			):



	"""simple docstring"""


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






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

		@cached_property
		def 		_UpperCAmelCase   (		self :      Dict       ):
			return "facebook/mask2former-swin-small-coco-instance"

		@cached_property
		def 		_UpperCAmelCase   (		self :      Tuple       ):
			return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None

		def 		_UpperCAmelCase   (		self :      Optional[int]       ):
			__a       =  MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(__SCREAMING_SNAKE_CASE       )
			__a       =  self.default_image_processor
			__a       =  prepare_img()
			__a       =  image_processor(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )
			__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(__SCREAMING_SNAKE_CASE     ,							(1, 3, 3_84, 3_84)       )

			with torch.no_grad():
				__a       =  model(**__SCREAMING_SNAKE_CASE       )

			__a       =  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(__SCREAMING_SNAKE_CASE       )
			self.assertTrue(
			    torch.allclose(
			        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=__SCREAMING_SNAKE_CASE       )       )

			__a       =  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(__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       =  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(__SCREAMING_SNAKE_CASE       )
			self.assertTrue(
			    torch.allclose(
			        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=__SCREAMING_SNAKE_CASE       )       )

		def 		_UpperCAmelCase   (		self :      str       ):
			__a       =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__SCREAMING_SNAKE_CASE       ).eval()
			__a       =  self.default_image_processor
			__a       =  prepare_img()
			__a       =  image_processor(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )
			__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(__SCREAMING_SNAKE_CASE     ,							(1, 3, 3_84, 3_84)       )

			with torch.no_grad():
				__a       =  model(**__SCREAMING_SNAKE_CASE       )
			# masks_queries_logits
			__a       =  outputs.masks_queries_logits
			self.assertEqual(
			    masks_queries_logits.shape     ,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
			__a       =  [
			    [-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],
			]
			__a       =  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       =  outputs.class_queries_logits
			self.assertEqual(class_queries_logits.shape     ,							(1, model.config.num_queries, model.config.num_labels + 1)       )
			__a       =  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(__SCREAMING_SNAKE_CASE       )
			self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=__SCREAMING_SNAKE_CASE       )       )

		def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
			__a       =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__SCREAMING_SNAKE_CASE       ).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(__SCREAMING_SNAKE_CASE       )
			__a       =  [el.to(__SCREAMING_SNAKE_CASE       ) for el in inputs["mask_labels"]]
			__a       =  [el.to(__SCREAMING_SNAKE_CASE       ) for el in inputs["class_labels"]]

			with torch.no_grad():
				__a       =  model(**__SCREAMING_SNAKE_CASE       )

			self.assertTrue(outputs.loss is not None       )



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


