
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     lowerCamelCase      :







	def __init__(     self      :List[str]   ,     lowercase      :str   ,     lowercase      :Union[str, Any]=3   ,     lowercase      :Any=3_2   ,     lowercase      :List[str]=3   ,     lowercase      :List[Any]=1_0   ,     lowercase      :Union[str, Any]=[8, 1_6, 3_2, 6_4]   ,     lowercase      :Tuple=[1, 1, 2, 1]   ,     lowercase      :str=True   ,     lowercase      :Tuple=True   ,     lowercase      :Tuple="relu"   ,     lowercase      :str=3   ,     lowercase      :int=None   ,     lowercase      :Optional[Any]=["stage2", "stage3", "stage4"]   ,     lowercase      :List[str]=[2, 3, 4]   ,     lowercase      :Any=1   ,     )		->      List[str]:







				"""simple docstring"""






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







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







				"""simple docstring"""






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

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

				SCREAMING_SNAKE_CASE     						= self.get_config()

				return config, pixel_values, labels







	def  snake_case__     (     self      :int			)		->      str:







				"""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  snake_case__     (     self      :Optional[int]   ,     lowercase      :str   ,     lowercase      :str   ,     lowercase      :Dict			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BitModel(config=lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(lowercase			)
				self.parent.assertEqual(
				    result.last_hidden_state.shape   ,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,     )







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Tuple   ,     lowercase      :Tuple   ,     lowercase      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Any   ,     lowercase      :str   ,     lowercase      :Tuple   ,     lowercase      :Dict			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BitBackbone(config=lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(lowercase			)

				# 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
				SCREAMING_SNAKE_CASE     						= None
				SCREAMING_SNAKE_CASE     						= BitBackbone(config=lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(lowercase			)

				# 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  snake_case__     (     self      :str			)		->      Optional[Any]:







				"""simple docstring"""






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





@require_torch
class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					Dict    		=	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	UpperCamelCase_  :					List[Any]    		=	(
	    {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	UpperCamelCase_  :					str    		=	False
	UpperCamelCase_  :					Optional[int]    		=	False
	UpperCamelCase_  :					Dict    		=	False
	UpperCamelCase_  :					Optional[int]    		=	False
	UpperCamelCase_  :					Optional[int]    		=	False







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BitModelTester(self			)
				SCREAMING_SNAKE_CASE     						= ConfigTester(self   ,     config_class=lowercase   ,     has_text_modality=lowercase			)







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







				"""simple docstring"""






				return







	@unittest.skip(reason='''Bit does not output attentions'''			)
	def  snake_case__     (     self      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				pass







	@unittest.skip(reason='''Bit does not use inputs_embeds'''			)
	def  snake_case__     (     self      :List[str]			)		->      Tuple:







				"""simple docstring"""






				pass







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







				"""simple docstring"""






				pass







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs_for_common()

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

							SCREAMING_SNAKE_CASE     						= ['''pixel_values''']
							self.assertListEqual(arg_names[:1]   ,     lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase			)







	def  snake_case__     (     self      :Optional[Any]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*lowercase			)







	def  snake_case__     (     self      :Dict			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE     						= model_class(config=lowercase			)
							for name, module in model.named_modules():
										if isinstance(lowercase   ,     (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  snake_case__     (     self      :Optional[int]			)		->      str:







				"""simple docstring"""






				def check_hidden_states_output(lowercase      :int   ,     lowercase      :Optional[Any]   ,     lowercase      :Optional[int]			):
							SCREAMING_SNAKE_CASE     						= model_class(lowercase			)
							model.to(lowercase			)
							model.eval()

							with torch.no_grad():
										SCREAMING_SNAKE_CASE     						= model(**self._prepare_for_class(lowercase   ,     lowercase			)			)

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

							SCREAMING_SNAKE_CASE     						= self.model_tester.num_stages
							self.assertEqual(len(lowercase			)   ,     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]   ,     )

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

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

										check_hidden_states_output(lowercase   ,     lowercase   ,     lowercase			)







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







				"""simple docstring"""






				pass







	def  snake_case__     (     self      :Dict			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase			)







	@slow
	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






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



def    a      ( )     ->Dict:




			'''simple docstring'''


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





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







	@cached_property
	def  snake_case__     (     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  snake_case__     (     self      :Dict			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.default_image_processor
				SCREAMING_SNAKE_CASE     						= prepare_img()
				SCREAMING_SNAKE_CASE     						= image_processor(images=lowercase   ,     return_tensors='''pt'''			).to(lowercase			)

				# forward pass
				with torch.no_grad():
							SCREAMING_SNAKE_CASE     						= model(**lowercase			)

				# verify the logits
				SCREAMING_SNAKE_CASE     						= torch.Size((1, 1_0_0_0)			)
				self.assertEqual(outputs.logits.shape   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]			).to(lowercase			)

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





@require_torch
class     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					List[str]    		=	(BitBackbone,) if is_torch_available() else ()
	UpperCamelCase_  :					Optional[Any]    		=	BitConfig

	UpperCamelCase_  :					List[Any]    		=	False







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BitModelTester(self			)

from typing import TYPE_CHECKING

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


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure)