


'''simple docstring'''



import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image






class  UpperCamelCase__:




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				str=3   ,					lowerCAmelCase    :				List[str]=32   ,					lowerCAmelCase    :				List[str]=3   ,					lowerCAmelCase    :				Union[str, Any]=10   ,					lowerCAmelCase    :				Optional[int]=[8, 16, 32, 64]   ,					lowerCAmelCase    :				Tuple=[1, 1, 2, 1]   ,					lowerCAmelCase    :				str=True   ,					lowerCAmelCase    :				List[str]=True   ,					lowerCAmelCase    :				Optional[int]="relu"   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=["stage2", "stage3", "stage4"]   ,					lowerCAmelCase    :				List[Any]=[2, 3, 4]   ,					lowerCAmelCase    :				int=1   ,					)->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			embeddings_size
										UpperCAmelCase         =			hidden_sizes
										UpperCAmelCase         =			depths
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			num_labels
										UpperCAmelCase         =			scope
										UpperCAmelCase         =			len(lowerCAmelCase   )
										UpperCAmelCase         =			out_features
										UpperCAmelCase         =			out_indices
										UpperCAmelCase         =			num_groups




				def   a__(	self    :				str   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.num_labels   )

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values, labels




				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										return BitConfig(
										    num_channels=self.num_channels   ,					embeddings_size=self.embeddings_size   ,					hidden_sizes=self.hidden_sizes   ,					depths=self.depths   ,					hidden_act=self.hidden_act   ,					num_labels=self.num_labels   ,					out_features=self.out_features   ,					out_indices=self.out_indices   ,					num_groups=self.num_groups   ,					)




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[int]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			BitModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,					)




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Tuple   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			BitForImageClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )




				def   a__(	self    :				int   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			BitBackbone(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps   )   ,					len(config.out_features   )   )
										self.parent.assertListEqual(list(result.feature_maps[0].shape   )   ,					[self.batch_size, self.hidden_sizes[1], 4, 4]   )

										# verify channels
										self.parent.assertEqual(len(model.channels   )   ,					len(config.out_features   )   )
										self.parent.assertListEqual(model.channels   ,					config.hidden_sizes[1:]   )

										# verify backbone works with out_features=None
										UpperCAmelCase         =			None
										UpperCAmelCase         =			BitBackbone(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps   )   ,					1   )
										self.parent.assertListEqual(list(result.feature_maps[0].shape   )   ,					[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

										# verify channels
										self.parent.assertEqual(len(model.channels   )   ,					1   )
										self.parent.assertListEqual(model.channels   ,					[config.hidden_sizes[-1]]   )





				def   a__(	self    :				Any   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			config_and_inputs
										UpperCAmelCase         =			{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Any							= (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				__magic_name__       :   Tuple							= (
				    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__       :   Union[str, Any]							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   int							= False
				__magic_name__       :   List[str]							= False




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			BitModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   )




				def   a__(	self    :				Tuple   )->  str:

										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




				def   a__(	self    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										return




				@unittest.skip(reason='''Bit does not output attentions'''   )
				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Bit does not use inputs_embeds'''   )
				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Bit does not support input and output embeddings'''   )
				def   a__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										pass




				def   a__(	self    :				Union[str, Any]   )->  int:

										"""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   )




				def   a__(	self    :				Union[str, Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(config=lowerCAmelCase   )
																for name, module in model.named_modules():
																						if isinstance(lowerCAmelCase   ,					(nn.BatchNormad, nn.GroupNorm)   ):
																												self.assertTrue(
																												    torch.all(module.weight == 1   )   ,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,					)
																												self.assertTrue(
																												    torch.all(module.bias == 0   )   ,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,					)




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

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ):
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																model.to(lowerCAmelCase   )
																model.eval()

																with torch.no_grad():
																						UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

																UpperCAmelCase         =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																UpperCAmelCase         =			self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase   )   ,					expected_num_stages + 1   )

																# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]   )   ,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,					)

										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			['''preactivation''', '''bottleneck''']
										for model_class in self.all_model_classes:
																for layer_type in layers_type:
																						UpperCAmelCase         =			layer_type
																						UpperCAmelCase         =			True
																						check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

																						# check that output_hidden_states also work using config
																						del inputs_dict["output_hidden_states"]
																						UpperCAmelCase         =			True

																						check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )




				@unittest.skip(reason='''Bit does not use feedforward chunking'''   )
				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										pass




				def   a__(	self    :				List[str]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )





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

										"""simple docstring"""




										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			BitModel.from_pretrained(lowerCAmelCase   )
																self.assertIsNotNone(lowerCAmelCase   )







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
						return image






@require_torch
@require_vision
class  UpperCamelCase__(     unittest.TestCase ):




				@cached_property
				def   a__(	self    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
										)





				@slow
				def   a__(	self    :				Optional[Any]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(lowerCAmelCase   )

										UpperCAmelCase         =			self.default_image_processor
										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			torch.Size((1, 1000)   )
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			torch.tensor([[-0.6526, -0.5263, -1.4398]]   ).to(lowerCAmelCase   )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,					lowerCAmelCase   ,					atol=1E-4   )   )







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[Any]							= (BitBackbone,) if is_torch_available() else ()
				__magic_name__       :   int							= BitConfig

				__magic_name__       :   Union[str, Any]							= False




				def   a__(	self    :				Union[str, Any]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			BitModelTester(self   )









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





