


"""simple docstring"""
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 	lowercase__				:





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





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

      _lowerCamelCase	:					List[Any]   =		torch.ones([self.batch_size, self.min_size, self.max_size]      ,   device=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		(
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,   device=SCREAMING_SNAKE_CASE) > 0.5
      ).float()
      _lowerCamelCase	:					Dict   =		(torch.rand((self.batch_size, self.num_labels)      ,   device=SCREAMING_SNAKE_CASE) > 0.5).long()

      _lowerCamelCase	:					Optional[int]   =		self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					List[str]   =		MaskaFormerConfig(
          hidden_size=self.hidden_dim      ,   )
      _lowerCamelCase	:					Any   =		self.num_queries
      _lowerCamelCase	:					int   =		self.num_labels

      _lowerCamelCase	:					int   =		[1, 1, 1, 1]
      _lowerCamelCase	:					Any   =		self.num_channels

      _lowerCamelCase	:					Optional[Any]   =		64
      _lowerCamelCase	:					str   =		128
      _lowerCamelCase	:					Optional[Any]   =		self.hidden_dim
      _lowerCamelCase	:					Any   =		self.hidden_dim
      _lowerCamelCase	:					List[Any]   =		self.hidden_dim
      return config





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		self.prepare_config_and_inputs()
      _lowerCamelCase	:					str   =		{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					str   =		output.encoder_hidden_states
      _lowerCamelCase	:					int   =		output.pixel_decoder_hidden_states
      _lowerCamelCase	:					Optional[int]   =		output.transformer_decoder_hidden_states

      self.parent.assertTrue(len(SCREAMING_SNAKE_CASE)      ,   len(config.backbone_config.depths))
      self.parent.assertTrue(len(SCREAMING_SNAKE_CASE)      ,   len(config.backbone_config.depths))
      self.parent.assertTrue(len(SCREAMING_SNAKE_CASE)      ,   config.decoder_layers)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False)  ->  List[str]:
      with torch.no_grad():
         _lowerCamelCase	:					Optional[int]   =		MaskaFormerModel(config=SCREAMING_SNAKE_CASE)
         model.to(SCREAMING_SNAKE_CASE)
         model.eval()

         _lowerCamelCase	:					Optional[int]   =		model(pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)

      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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					str   =		MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

      def comm_check_on_output(SCREAMING_SNAKE_CASE):
         # 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():
         _lowerCamelCase	:					List[Any]   =		model(pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)

         comm_check_on_output(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Optional[Any]   =		model(
             pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE)

      comm_check_on_output(SCREAMING_SNAKE_CASE)

      self.parent.assertTrue(result.loss is not None)
      self.parent.assertEqual(result.loss.shape      ,   torch.Size([1]))







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   __UpperCAmelCase							       =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		MaskaFormerModelTester(self)
      _lowerCamelCase	:					Union[str, Any]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   has_text_modality=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE)





   @unittest.skip(reason="""Mask2Former does not use inputs_embeds""")
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      pass





   @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""")
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      pass





   @unittest.skip(reason="""Mask2Former is not a generative model""")
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      pass





   @unittest.skip(reason="""Mask2Former does not use token embeddings""")
   def 		UpperCamelCase_  (							self)  ->  Any:
      pass





   @require_torch_multi_gpu
   @unittest.skip(
       reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""")
   def 		UpperCamelCase_  (							self)  ->  Dict:
      pass





   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      pass





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCamelCase	:					int   =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   SCREAMING_SNAKE_CASE)





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





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Dict   =		(self.model_tester.min_size,) * 2
      _lowerCamelCase	:					str   =		{
          """pixel_values""": torch.randn((2, 3, *size)      ,   device=SCREAMING_SNAKE_CASE),
          """mask_labels""": torch.randn((2, 10, *size)      ,   device=SCREAMING_SNAKE_CASE),
          """class_labels""": torch.zeros(2      ,   10      ,   device=SCREAMING_SNAKE_CASE).long(),
      }
      _lowerCamelCase	:					List[str]   =		self.model_tester.get_config()

      _lowerCamelCase	:					Tuple   =		MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE)
      self.assertTrue(outputs.loss is not None)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE      ,   output_attentions=SCREAMING_SNAKE_CASE)
         self.assertTrue(outputs.attentions is not None)





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

      _lowerCamelCase	:					Any   =		self.all_model_classes[1]
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs()

      _lowerCamelCase	:					List[Any]   =		model_class(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.train()

      _lowerCamelCase	:					int   =		model(SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE).loss
      loss.backward()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		self.all_model_classes[1]
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.model_tester.prepare_config_and_inputs()
      _lowerCamelCase	:					int   =		True
      _lowerCamelCase	:					Optional[Any]   =		True

      _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      model.train()

      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      _lowerCamelCase	:					int   =		outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()

      _lowerCamelCase	:					str   =		outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      _lowerCamelCase	:					Optional[int]   =		outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE)

      self.assertIsNotNone(encoder_hidden_states.grad)
      self.assertIsNotNone(pixel_decoder_hidden_states.grad)
      self.assertIsNotNone(transformer_decoder_hidden_states.grad)
      self.assertIsNotNone(attentions.grad)







UpperCAmelCase				=						1e-4

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image





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





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  int:
      return "facebook/mask2former-swin-small-coco-instance"





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Tuple   =		MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		self.default_image_processor
      _lowerCamelCase	:					List[str]   =		prepare_img()
      _lowerCamelCase	:					Union[str, Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		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(SCREAMING_SNAKE_CASE      ,   (1, 3, 384, 384))

      with torch.no_grad():
         _lowerCamelCase	:					Dict   =		model(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		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(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Any   =		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(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Dict   =		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(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
      _lowerCamelCase	:					Optional[Any]   =		self.default_image_processor
      _lowerCamelCase	:					Any   =		prepare_img()
      _lowerCamelCase	:					Dict   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		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(SCREAMING_SNAKE_CASE      ,   (1, 3, 384, 384))

      with torch.no_grad():
         _lowerCamelCase	:					List[str]   =		model(**SCREAMING_SNAKE_CASE)
      # masks_queries_logits
      _lowerCamelCase	:					str   =		outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape      ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
      _lowerCamelCase	:					Any   =		[
          [-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],
      ]
      _lowerCamelCase	:					List[Any]   =		torch.tensor(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))
      # class_queries_logits
      _lowerCamelCase	:					List[str]   =		outputs.class_queries_logits
      self.assertEqual(class_queries_logits.shape      ,   (1, model.config.num_queries, model.config.num_labels + 1))
      _lowerCamelCase	:					Optional[Any]   =		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(SCREAMING_SNAKE_CASE)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Tuple   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
      _lowerCamelCase	:					str   =		self.default_image_processor

      _lowerCamelCase	:					Tuple   =		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"""      ,   )

      _lowerCamelCase	:					Optional[Any]   =		inputs["""pixel_values"""].to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		[el.to(SCREAMING_SNAKE_CASE) for el in inputs["""mask_labels"""]]
      _lowerCamelCase	:					Union[str, Any]   =		[el.to(SCREAMING_SNAKE_CASE) for el in inputs["""class_labels"""]]

      with torch.no_grad():
         _lowerCamelCase	:					Any   =		model(**SCREAMING_SNAKE_CASE)

      self.assertTrue(outputs.loss is not None)


"""simple docstring"""






from typing import TYPE_CHECKING

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


__lowerCAmelCase       :			Optional[int]   	={
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPFeatureExtractor"""]
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			Tuple   	=[
           """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ChineseCLIPModel""",
           """ChineseCLIPPreTrainedModel""",
           """ChineseCLIPTextModel""",
           """ChineseCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_chinese_clip import (
           CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ChineseCLIPConfig,
           ChineseCLIPOnnxConfig,
           ChineseCLIPTextConfig,
           ChineseCLIPVisionConfig,
       )
       from .processing_chinese_clip import ChineseCLIPProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_chinese_clip import (
                  CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ChineseCLIPModel,
                  ChineseCLIPPreTrainedModel,
                  ChineseCLIPTextModel,
                  ChineseCLIPVisionModel,
              )

else:
       import sys

       __lowerCAmelCase       :			Optional[Any]   	=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



