
'''simple docstring'''
import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
     from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







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



   """simple docstring"""





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



   """simple docstring"""





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

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

   __A		:  Optional[Any]       				=		self.get_config()

   return config, pixel_values, labels
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   return SwinConfig(
       image_size=self.image_size					, patch_size=self.patch_size					, num_channels=self.num_channels					, embed_dim=self.embed_dim					, 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 		lowercase_( self      :     Any					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     str					, lowerCamelCase      :     Any							):



   """simple docstring"""





   __A		:  Dict       				=		SwinModel(config=lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()
   __A		:  Optional[int]       				=		model(lowerCAmelCase_							)

   __A		:  str       				=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
   __A		:  Dict       				=		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 		lowercase_( self      :     Tuple					, lowerCamelCase      :     List[Any]					, lowerCamelCase      :     int					, lowerCamelCase      :     Optional[int]							):



   """simple docstring"""





   __A		:  List[Any]       				=		SwinBackbone(config=lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()
   __A		:  Dict       				=		model(lowerCAmelCase_							)

   # verify hidden states
   self.parent.assertEqual(len(result.feature_maps							)					, len(config.out_features							)							)
   self.parent.assertListEqual(list(result.feature_maps[0].shape							)					, [self.batch_size, model.channels[0], 16, 16]							)

   # verify channels
   self.parent.assertEqual(len(model.channels							)					, len(config.out_features							)							)

   # verify backbone works with out_features=None
   __A		:  Optional[int]       				=		None
   __A		:  Optional[int]       				=		SwinBackbone(config=lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()
   __A		:  Optional[Any]       				=		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, model.channels[-1], 4, 4]							)

   # verify channels
   self.parent.assertEqual(len(model.channels							)					, 1							)
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     List[str]							):



   """simple docstring"""





   __A		:  Any       				=		SwinForMaskedImageModeling(config=lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()
   __A		:  Optional[int]       				=		model(lowerCAmelCase_							)
   self.parent.assertEqual(
       result.logits.shape					, (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

   # test greyscale images
   __A		:  List[str]       				=		1
   __A		:  Any       				=		SwinForMaskedImageModeling(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()

   __A		:  Optional[int]       				=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
   __A		:  Optional[Any]       				=		model(lowerCAmelCase_							)
   self.parent.assertEqual(result.logits.shape					, (self.batch_size, 1, self.image_size, self.image_size)							)
 def 		lowercase_( self      :     Tuple					, lowerCamelCase      :     Any					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Optional[int]							):



   """simple docstring"""





   __A		:  int       				=		self.type_sequence_label_size
   __A		:  int       				=		SwinForImageClassification(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()
   __A		:  Any       				=		model(lowerCAmelCase_					, labels=lowerCAmelCase_							)
   self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)							)

   # test greyscale images
   __A		:  Any       				=		1
   __A		:  List[Any]       				=		SwinForImageClassification(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()

   __A		:  Tuple       				=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
   __A		:  Dict       				=		model(lowerCAmelCase_							)
   self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)							)






 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  Any       				=		self.prepare_config_and_inputs()
   (
       (
       __A  
   )				,       (
       __A  
   )				,       (
       __A  
   )				,         
   )		:  Union[str, Any]       				=		config_and_inputs
   __A		:  List[Any]       				=		{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class 			__A					(			lowerCamelCase_     ,    lowerCamelCase_     ,    unittest.TestCase      ):
 lowerCamelCase												=(
     (
         SwinModel,
         SwinBackbone,
         SwinForImageClassification,
         SwinForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 lowerCamelCase												=(
     {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
     if is_torch_available()
     else {}
 )
 lowerCamelCase												=True

 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  int       				=		SwinModelTester(self							)
   __A		:  Dict       				=		ConfigTester(self					, config_class=lowerCAmelCase_					, embed_dim=37							)
 def 		lowercase_( self      :     Dict							):



   """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 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   return
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCAmelCase_							)
 @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""							)
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   pass
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   super().test_training_gradient_checkpointing()
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Dict       				=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*lowerCAmelCase_							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Tuple       				=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_							)
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  List[Any]       				=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)
 @unittest.skip(reason="""Swin does not use inputs_embeds"""							)
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   pass
 @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""							)
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   pass
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A				,       __A		:  Tuple       				=		self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __A		:  Optional[int]       				=		model_class(lowerCAmelCase_							)
     self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)							)
     __A		:  Union[str, Any]       				=		model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCAmelCase_					, nn.Linear							)							)
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





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

   for model_class in self.all_model_classes:
     __A		:  List[str]       				=		model_class(lowerCAmelCase_							)
     __A		:  Union[str, Any]       				=		inspect.signature(model.forward							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __A		:  Optional[int]       				=		[*signature.parameters.keys()]

     __A		:  List[Any]       				=		["""pixel_values"""]
     self.assertListEqual(arg_names[:1]					, lowerCAmelCase_							)
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A				,       __A		:  Union[str, Any]       				=		self.model_tester.prepare_config_and_inputs_for_common()
   __A		:  Optional[Any]       				=		True

   for model_class in self.all_model_classes:
     __A		:  Tuple       				=		True
     __A		:  List[Any]       				=		False
     __A		:  Dict       				=		True
     __A		:  Optional[int]       				=		model_class(lowerCAmelCase_							)
     model.to(lowerCAmelCase_							)
     model.eval()
     with torch.no_grad():
       __A		:  Optional[Any]       				=		model(**self._prepare_for_class(lowerCAmelCase_					, lowerCAmelCase_							)							)
     __A		:  Union[str, Any]       				=		outputs.attentions
     __A		:  Any       				=		len(self.model_tester.depths							)
     self.assertEqual(len(lowerCAmelCase_							)					, lowerCAmelCase_							)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     __A		:  int       				=		True
     __A		:  Dict       				=		config.window_size**2
     __A		:  Optional[int]       				=		model_class(lowerCAmelCase_							)
     model.to(lowerCAmelCase_							)
     model.eval()
     with torch.no_grad():
       __A		:  str       				=		model(**self._prepare_for_class(lowerCAmelCase_					, lowerCAmelCase_							)							)
     __A		:  Optional[Any]       				=		outputs.attentions
     self.assertEqual(len(lowerCAmelCase_							)					, lowerCAmelCase_							)

     self.assertListEqual(
         list(attentions[0].shape[-3:]							)					, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					, )
     __A		:  Optional[int]       				=		len(lowerCAmelCase_							)

     # Check attention is always last and order is fine
     __A		:  Optional[int]       				=		True
     __A		:  Tuple       				=		True
     __A		:  Dict       				=		model_class(lowerCAmelCase_							)
     model.to(lowerCAmelCase_							)
     model.eval()
     with torch.no_grad():
       __A		:  Any       				=		model(**self._prepare_for_class(lowerCAmelCase_					, lowerCAmelCase_							)							)

     # also another +1 for reshaped_hidden_states
     __A		:  Any       				=		1 if model_class.__name__ == """SwinBackbone""" else 2
     self.assertEqual(out_len + added_hidden_states					, len(lowerCAmelCase_							)							)

     __A		:  Optional[int]       				=		outputs.attentions

     self.assertEqual(len(lowerCAmelCase_							)					, lowerCAmelCase_							)

     self.assertListEqual(
         list(self_attentions[0].shape[-3:]							)					, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					, )
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     Dict					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		model_class(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)
   model.eval()

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

   __A		:  Tuple       				=		outputs.hidden_states

   __A		:  List[Any]       				=		getattr(
       self.model_tester					, """expected_num_hidden_layers"""					, len(self.model_tester.depths							) + 1							)
   self.assertEqual(len(lowerCAmelCase_							)					, lowerCAmelCase_							)

   # Swin has a different seq_length
   __A		:  List[Any]       				=		(
       config.patch_size
       if isinstance(config.patch_size					, collections.abc.Iterable							)
       else (config.patch_size, config.patch_size)
   )

   __A		:  Tuple       				=		(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]					, )

   if not model_class.__name__ == "SwinBackbone":
     __A		:  Dict       				=		outputs.reshaped_hidden_states
     self.assertEqual(len(lowerCAmelCase_							)					, lowerCAmelCase_							)

     __A				,       __A				,       __A				,       __A		:  Optional[Any]       				=		reshaped_hidden_states[0].shape
     __A		:  List[Any]       				=		(
         reshaped_hidden_states[0].view(lowerCAmelCase_					, lowerCAmelCase_					, height * width							).permute(0					, 2					, 1							)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]							)					, [num_patches, self.model_tester.embed_dim]					, )
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A				,       __A		:  Dict       				=		self.model_tester.prepare_config_and_inputs_for_common()

   __A		:  Optional[int]       				=		(
       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:
     __A		:  Dict       				=		True
     self.check_hidden_states_output(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_							)

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

     self.check_hidden_states_output(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_							)
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A				,       __A		:  Tuple       				=		self.model_tester.prepare_config_and_inputs_for_common()
   __A		:  int       				=		3

   __A		:  Union[str, 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)
   )
   __A		:  Optional[int]       				=		(
       config.patch_size
       if isinstance(config.patch_size					, collections.abc.Iterable							)
       else (config.patch_size, config.patch_size)
   )

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

   for model_class in self.all_model_classes:
     __A		:  Union[str, Any]       				=		True
     self.check_hidden_states_output(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, (padded_height, padded_width)							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __A		:  Optional[Any]       				=		True
     self.check_hidden_states_output(lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, (padded_height, padded_width)							)
 @slow
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __A		:  int       				=		SwinModel.from_pretrained(lowerCAmelCase_							)
     self.assertIsNotNone(lowerCAmelCase_							)






 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





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

   __A		:  Tuple       				=		_config_zero_init(lowerCAmelCase_							)
   for model_class in self.all_model_classes:
     __A		:  Union[str, Any]       				=		model_class(config=lowerCAmelCase_							)
     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 			__A					(			unittest.TestCase      ):
 @cached_property
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   return (
       AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							)
       if is_vision_available()
       else None
   )






 @slow
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							).to(lowerCAmelCase_							)
   __A		:  List[str]       				=		self.default_image_processor

   __A		:  List[Any]       				=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
   __A		:  str       				=		image_processor(images=lowerCAmelCase_					, return_tensors="""pt"""							).to(lowerCAmelCase_							)

   # forward pass
   with torch.no_grad():
     __A		:  Dict       				=		model(**lowerCAmelCase_							)

   # verify the logits
   __A		:  List[Any]       				=		torch.Size((1, 10_00)							)
   self.assertEqual(outputs.logits.shape					, lowerCAmelCase_							)
   __A		:  List[str]       				=		torch.tensor([-0.0_948, -0.6_454, -0.0_921]							).to(lowerCAmelCase_							)
   self.assertTrue(torch.allclose(outputs.logits[0, :3]					, lowerCAmelCase_					, atol=1e-4							)							)







@require_torch
class 			__A					(			unittest.TestCase     ,    lowerCamelCase_      ):
 lowerCamelCase												=(SwinBackbone,) if is_torch_available() else ()
 lowerCamelCase												=SwinConfig
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  List[Any]       				=		SwinModelTester(self							)




from typing import TYPE_CHECKING

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


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						lowerCAmelCase__  	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




