
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   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Any		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:str=2		,    lowerCAmelCase__							:Any=True		,    lowerCAmelCase__							:List[str]=False		,    lowerCAmelCase__							:Tuple=10		,    lowerCAmelCase__							:int=3		,    lowerCAmelCase__							:int=32 * 8		,    lowerCAmelCase__							:Optional[Any]=32 * 8		,    lowerCAmelCase__							:List[Any]=4		,    lowerCAmelCase__							:Optional[Any]=64		,    )  ->int:
										lowercase         =      parent
										lowercase         =      batch_size
										lowercase         =      is_training
										lowercase         =      use_auxiliary_loss
										lowercase         =      num_queries
										lowercase         =      num_channels
										lowercase         =      min_size
										lowercase         =      max_size
										lowercase         =      num_labels
										lowercase         =      hidden_dim
										lowercase         =      hidden_dim





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										lowercase         =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]     ).to(
										    lowerCAmelCase__     )

										lowercase         =      torch.ones([self.batch_size, self.min_size, self.max_size]		,    device=lowerCAmelCase__     )

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

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Optional[Any]:
										lowercase         =      MaskaFormerConfig(
										    hidden_size=self.hidden_dim		,    )
										lowercase         =      self.num_queries
										lowercase         =      self.num_labels

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

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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.prepare_config_and_inputs()
										lowercase         =      {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
										return config, inputs_dict





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										lowercase         =      output.encoder_hidden_states
										lowercase         =      output.pixel_decoder_hidden_states
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Tuple=False     )  ->Any:
										with torch.no_grad():
														lowercase         =      MaskaFormerModel(config=lowerCAmelCase__     )
														model.to(lowerCAmelCase__     )
														model.eval()

														lowercase         =      model(pixel_values=lowerCAmelCase__		,    pixel_mask=lowerCAmelCase__     )
														lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]     )  ->Dict:
										lowercase         =      MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()

										def comm_check_on_output(lowerCAmelCase__							:List[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():
														lowercase         =      model(pixel_values=lowerCAmelCase__		,    pixel_mask=lowerCAmelCase__     )
														lowercase         =      model(lowerCAmelCase__     )

														comm_check_on_output(lowerCAmelCase__     )

														lowercase         =      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   UpperCamelCase_      (      __a		,    __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
						UpperCamelCase      :  str				   =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

						UpperCamelCase      :  Optional[int]				   =					False
						UpperCamelCase      :  Any				   =					False
						UpperCamelCase      :  Tuple				   =					False
						UpperCamelCase      :  Optional[int]				   =					False





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										lowercase         =      MaskaFormerModelTester(self     )
										lowercase         =      ConfigTester(self		,    config_class=lowerCAmelCase__		,    has_text_modality=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										self.config_tester.run_common_tests()





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__		,    **lowerCAmelCase__		,    output_hidden_states=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Tuple:
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->List[str]:
										pass





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





						@unittest.skip(reason="Mask2Former is not a generative model"     )
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Union[str, Any]:
										pass





						@unittest.skip(reason="Mask2Former does not use token embeddings"     )
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Tuple:
										pass





						@require_torch_multi_gpu
						@unittest.skip(
						    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"     )
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, 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							:Union[str, Any]     )  ->Optional[Any]:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()

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

														lowercase         =      ["pixel_values"]
														self.assertListEqual(arg_names[:1]		,    lowerCAmelCase__     )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Any:
										for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														lowercase         =      MaskaFormerModel.from_pretrained(lowerCAmelCase__     )
														self.assertIsNotNone(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										lowercase         =      (self.model_tester.min_size,) * 2
										lowercase         =      {
										    "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(),
										}
										lowercase         =      self.model_tester.get_config()

										lowercase         =      MaskaFormerForUniversalSegmentation(lowerCAmelCase__     ).to(lowerCAmelCase__     )
										lowercase         =      model(**lowerCAmelCase__     )
										self.assertTrue(outputs.loss is not None     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__		,    **lowerCAmelCase__		,    output_hidden_states=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Union[str, Any]:
										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->List[str]:
										if not self.model_tester.is_training:
														return

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

										lowercase         =      model_class(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.train()

										lowercase         =      model(lowerCAmelCase__		,    mask_labels=lowerCAmelCase__		,    class_labels=lowerCAmelCase__     ).loss
										loss.backward()





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Tuple:
										lowercase         =      self.all_model_classes[1]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs()
										lowercase         =      True
										lowercase         =      True

										lowercase         =      model_class(lowerCAmelCase__     ).to(lowerCAmelCase__     )
										model.train()

										lowercase         =      model(lowerCAmelCase__		,    mask_labels=lowerCAmelCase__		,    class_labels=lowerCAmelCase__     )

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

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

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

										lowercase         =      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     )




_snake_case      :							Union[str, Any]     	=    1e-4

def  __snake_case							(	):







				'''simple docstring'''


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





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







						'''simple docstring'''



						@cached_property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[int]:
										return "facebook/mask2former-swin-small-coco-instance"





						@cached_property
						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->List[Any]:
										return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints     ) if is_vision_available() else None





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->int:
										lowercase         =      MaskaFormerModel.from_pretrained(self.model_checkpoints     ).to(lowerCAmelCase__     )
										lowercase         =      self.default_image_processor
										lowercase         =      prepare_img()
										lowercase         =      image_processor(lowerCAmelCase__		,    return_tensors="pt"     ).to(lowerCAmelCase__     )
										lowercase         =      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():
														lowercase         =      model(**lowerCAmelCase__     )

										lowercase         =      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__     )     )

										lowercase         =      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__     )     )

										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:str     )  ->Union[str, Any]:
										lowercase         =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(lowerCAmelCase__     ).eval()
										lowercase         =      self.default_image_processor
										lowercase         =      prepare_img()
										lowercase         =      image_processor(lowerCAmelCase__		,    return_tensors="pt"     ).to(lowerCAmelCase__     )
										lowercase         =      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():
														lowercase         =      model(**lowerCAmelCase__     )
										# masks_queries_logits
										lowercase         =      outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape		,    (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     )
										lowercase         =      [
										    [-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],
										]
										lowercase         =      torch.tensor(lowerCAmelCase__     ).to(lowerCAmelCase__     )
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,    lowerCAmelCase__		,    atol=lowerCAmelCase__     )     )
										# class_queries_logits
										lowercase         =      outputs.class_queries_logits
										self.assertEqual(class_queries_logits.shape		,    (1, model.config.num_queries, model.config.num_labels + 1)     )
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[Any]:
										lowercase         =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(lowerCAmelCase__     ).eval()
										lowercase         =      self.default_image_processor

										lowercase         =      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"		,    )

										lowercase         =      inputs["pixel_values"].to(lowerCAmelCase__     )
										lowercase         =      [el.to(lowerCAmelCase__     ) for el in inputs["mask_labels"]]
										lowercase         =      [el.to(lowerCAmelCase__     ) for el in inputs["class_labels"]]

										with torch.no_grad():
														lowercase         =      model(**lowerCAmelCase__     )

										self.assertTrue(outputs.loss is not None     )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "WavLMForAudioFrameClassification",
						    "WavLMForCTC",
						    "WavLMForSequenceClassification",
						    "WavLMForXVector",
						    "WavLMModel",
						    "WavLMPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
