

import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    FocalNetBackbone,
			    FocalNetForImageClassification,
			    FocalNetForMaskedImageModeling,
			    FocalNetModel,
			)
			from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Tuple      ,      a							: Union[str, Any]      ,      a							: Dict=13      ,      a							: str=32      ,      a							: List[Any]=2      ,      a							: Optional[Any]=3      ,      a							: Optional[Any]=16      ,      a							: Optional[int]=[32, 64, 128]      ,      a							: Union[str, Any]=[1, 2, 1]      ,      a							: List[str]=[2, 2, 4]      ,      a							: Dict=2      ,      a							: Optional[int]=2.0      ,      a							: Tuple=True      ,      a							: Optional[Any]=0.0      ,      a							: List[str]=0.0      ,      a							: List[str]=0.1      ,      a							: Any="gelu"      ,      a							: Optional[int]=False      ,      a							: List[str]=True      ,      a							: Optional[int]=0.02      ,      a							: Tuple=1e-5      ,      a							: int=True      ,      a							: List[Any]=None      ,      a							: List[str]=True      ,      a							: Dict=10      ,      a							: List[str]=8      ,      a							: Tuple=["stage1", "stage2"]      ,      a							: Dict=[1, 2]      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       parent
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       patch_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      List[str]				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_sizes
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       depths
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_heads
				SCREAMING_SNAKE_CASE   :      List[str]				     =       window_size
				SCREAMING_SNAKE_CASE   :      Any				     =       mlp_ratio
				SCREAMING_SNAKE_CASE   :      str				     =       qkv_bias
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      Tuple				     =       drop_path_rate
				SCREAMING_SNAKE_CASE   :      Tuple				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_absolute_embeddings
				SCREAMING_SNAKE_CASE   :      Any				     =       patch_norm
				SCREAMING_SNAKE_CASE   :      Dict				     =       layer_norm_eps
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       is_training
				SCREAMING_SNAKE_CASE   :      str				     =       scope
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       use_labels
				SCREAMING_SNAKE_CASE   :      int				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      int				     =       encoder_stride
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       out_features
				SCREAMING_SNAKE_CASE   :      Dict				     =       out_indices
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.get_config()

				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				return FocalNetConfig(
				    image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      hidden_sizes=self.hidden_sizes      ,      depths=self.depths      ,      num_heads=self.num_heads      ,      window_size=self.window_size      ,      mlp_ratio=self.mlp_ratio      ,      qkv_bias=self.qkv_bias      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      drop_path_rate=self.drop_path_rate      ,      hidden_act=self.hidden_act      ,      use_absolute_embeddings=self.use_absolute_embeddings      ,      path_norm=self.patch_norm      ,      layer_norm_eps=self.layer_norm_eps      ,      initializer_range=self.initializer_range      ,      encoder_stride=self.encoder_stride      ,      out_features=self.out_features      ,      out_indices=self.out_indices      ,      )
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any      ,      a							: Any      ,      a							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       FocalNetModel(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      int				     =       model(a	)

				SCREAMING_SNAKE_CASE   :      int				     =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
				SCREAMING_SNAKE_CASE   :      Tuple				     =       int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, expected_seq_len, expected_dim)	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: List[Any]      ,      a							: Union[str, Any]      ,      a							: Optional[int]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       FocalNetBackbone(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       model(a	)

				# 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.image_size, 8, 8]	)

				# 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   :      List[str]				     =       None
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       FocalNetBackbone(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      int				     =       model(a	)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps	)      ,      1	)
				self.parent.assertListEqual(list(result.feature_maps[0].shape	)      ,      [self.batch_size, self.image_size * 2, 4, 4]	)

				# verify channels
				self.parent.assertEqual(len(model.channels	)      ,      1	)
				self.parent.assertListEqual(model.channels      ,      [config.hidden_sizes[-1]]	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       FocalNetForMaskedImageModeling(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       model(a	)
				self.parent.assertEqual(
				    result.reconstruction.shape      ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

				# test greyscale images
				SCREAMING_SNAKE_CASE   :      str				     =       1
				SCREAMING_SNAKE_CASE   :      List[str]				     =       FocalNetForMaskedImageModeling(a	)
				model.to(a	)
				model.eval()

				SCREAMING_SNAKE_CASE   :      Any				     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       model(a	)
				self.parent.assertEqual(result.reconstruction.shape      ,      (self.batch_size, 1, self.image_size, self.image_size)	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Optional[Any]      ,      a							: Any      ,      a							: List[str]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       FocalNetForImageClassification(a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(a      ,      labels=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)	)

				# test greyscale images
				SCREAMING_SNAKE_CASE   :      Dict				     =       1
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       FocalNetForImageClassification(a	)
				model.to(a	)
				model.eval()

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				SCREAMING_SNAKE_CASE   :      int				     =       model(a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)	)






			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.prepare_config_and_inputs()

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      Tuple				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        FocalNetModel,
			        FocalNetForImageClassification,
			        FocalNetForMaskedImageModeling,
			        FocalNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__           =(
			    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__           =False

			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       FocalNetModelTester(self	)
				SCREAMING_SNAKE_CASE   :      int				     =       ConfigTester(self      ,      config_class=a      ,      embed_dim=37      ,      has_text_modality=a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[int]:

				"""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 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				return
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*a	)
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*a	)
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)
			@unittest.skip(reason="FocalNet does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="FocalNet does not use feedforward chunking"	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: str	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      int				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes[:-1]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       model_class(a	)
					self.assertIsInstance(model.get_input_embeddings()      ,      (nn.Module)	)
					SCREAMING_SNAKE_CASE   :      Tuple				     =       model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(a      ,      nn.Linear	)	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      int				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes[:-1]:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       inspect.signature(model.forward	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE   :      int				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Optional[int]      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_class(a	)
				model.to(a	)
				model.eval()

				with torch.no_grad():
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(**self._prepare_for_class(a      ,      a	)	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       outputs.hidden_states

				SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(
				    self.model_tester      ,      "expected_num_hidden_layers"      ,      len(self.model_tester.depths	) + 1	)
				self.assertEqual(len(a	)      ,      a	)

				# FocalNet has a different seq_length
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (
				    config.patch_size
				    if isinstance(config.patch_size      ,      collections.abc.Iterable	)
				    else (config.patch_size, config.patch_size)
				)

				SCREAMING_SNAKE_CASE   :      Dict				     =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]	)      ,      [num_patches, self.model_tester.embed_dim]      ,      )

				SCREAMING_SNAKE_CASE   :      List[str]				     =       outputs.reshaped_hidden_states
				self.assertEqual(len(a	)      ,      a	)

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       reshaped_hidden_states[0].shape
				SCREAMING_SNAKE_CASE   :      str				     =       (
				    reshaped_hidden_states[0].view(a      ,      a      ,      height * width	).permute(0      ,      2      ,      1	)
				)
				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-2:]	)      ,      [num_patches, self.model_tester.embed_dim]      ,      )
			def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				SCREAMING_SNAKE_CASE   :      Any				     =       (
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable	)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)

				for model_class in self.all_model_classes[:-1]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       True
					self.check_hidden_states_output(a      ,      a      ,      a      ,      a	)

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

					self.check_hidden_states_output(a      ,      a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       3

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable	)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       (
				    config.patch_size
				    if isinstance(config.patch_size      ,      collections.abc.Iterable	)
				    else (config.patch_size, config.patch_size)
				)

				SCREAMING_SNAKE_CASE   :      Any				     =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes[:-1]:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
					self.check_hidden_states_output(a      ,      a      ,      a      ,      (padded_height, padded_width)	)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE   :      List[str]				     =       True
					self.check_hidden_states_output(a      ,      a      ,      a      ,      (padded_height, padded_width)	)
			@slow
			def 							__UpperCamelCase (			self							: int	)							->       Union[str, Any]:

				"""simple docstring"""

				for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      int				     =       FocalNetModel.from_pretrained(a	)
					self.assertIsNotNone(a	)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				SCREAMING_SNAKE_CASE   :      int				     =       _config_zero_init(a	)
				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model_class(config=a	)
					for name, param in model.named_parameters():
						if "embeddings" not in name and param.requires_grad:
							self.assertIn(
							    ((param.data.mean() * 1e9).round() / 1e9).item()      ,      [0.0, 1.0]      ,      msg=F"Parameter {name} of model {model_class} seems not properly initialized"      ,      )






@require_vision
@require_torch
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







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

				"""simple docstring"""

				return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"	) if is_vision_available() else None






			@slow
			def 							__UpperCamelCase (			self							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"	).to(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.default_image_processor

				SCREAMING_SNAKE_CASE   :      Any				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       image_processor(images=a      ,      return_tensors="pt"	).to(a	)

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

				# verify the logits
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.Size((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([0.2166, -0.4368, 0.2191]	).to(a	)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,      a      ,      atol=1e-4	)	)
				self.assertTrue(outputs.logits.argmax(dim=-1	).item()      ,      281	)






@require_torch
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(FocalNetBackbone,) if is_torch_available() else ()
			lowerCamelCase__           =FocalNetConfig

			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       FocalNetModelTester(self	)


from typing import TYPE_CHECKING

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


a_						     =    {
    '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:
			a_						     =    ['BridgeTowerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    '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

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