
import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import YolosImageProcessor





class 							A__		(	unittest.TestCase):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=7    ,  __magic_name__=3    ,  __magic_name__=3_0    ,  __magic_name__=4_0_0    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=True    ,  __magic_name__=[0.5, 0.5, 0.5]    ,  __magic_name__=[0.5, 0.5, 0.5]    ,  __magic_name__=True    ,  __magic_name__=1 / 2_5_5    ,  __magic_name__=True    ,  ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowerCamelCase		:	Any							=      size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
       lowerCamelCase		:	Optional[Any]							=      parent
       lowerCamelCase		:	Union[str, Any]							=      batch_size
       lowerCamelCase		:	List[Any]							=      num_channels
       lowerCamelCase		:	List[str]							=      min_resolution
       lowerCamelCase		:	Optional[int]							=      max_resolution
       lowerCamelCase		:	int							=      do_resize
       lowerCamelCase		:	Dict							=      size
       lowerCamelCase		:	List[str]							=      do_normalize
       lowerCamelCase		:	int							=      image_mean
       lowerCamelCase		:	List[str]							=      image_std
       lowerCamelCase		:	Union[str, Any]							=      do_rescale
       lowerCamelCase		:	int							=      rescale_factor
       lowerCamelCase		:	Dict							=      do_pad
      def 						UpperCamelCase__							(      self      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=False      ):
       if not batched:
        lowerCamelCase		:	Any							=      image_inputs[0]
        if isinstance(__magic_name__    ,  Image.Image      ):
         lowerCamelCase			,    lowerCamelCase		:	int							=      image.size
        else:
         lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      image.shape[1], image.shape[2]
        if w < h:
         lowerCamelCase		:	List[Any]							=      int(self.size["""shortest_edge"""] * h / w      )
         lowerCamelCase		:	Tuple							=      self.size["""shortest_edge"""]
        elif w > h:
         lowerCamelCase		:	str							=      self.size["""shortest_edge"""]
         lowerCamelCase		:	int							=      int(self.size["""shortest_edge"""] * w / h      )
        else:
         lowerCamelCase		:	Any							=      self.size["""shortest_edge"""]
         lowerCamelCase		:	Optional[Any]							=      self.size["""shortest_edge"""]

       else:
        lowerCamelCase		:	Any							=      []
        for image in image_inputs:
         lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      self.get_expected_values([image]      )
         expected_values.append((expected_height, expected_width)      )
        lowerCamelCase		:	List[Any]							=      max(__magic_name__    ,  key=lambda __magic_name__      : item[0]      )[0]
        lowerCamelCase		:	str							=      max(__magic_name__    ,  key=lambda __magic_name__      : item[1]      )[1]

       return expected_height, expected_width





@require_torch
@require_vision
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    List[str]           =			YolosImageProcessor if is_vision_available() else None
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      YolosImageProcessingTester(self      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(__magic_name__    ,  """image_mean"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """image_std"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """do_normalize"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """do_resize"""      )      )
       self.assertTrue(hasattr(__magic_name__    ,  """size"""      )      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size    ,  {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}      )
       self.assertEqual(image_processor.do_pad    ,  __magic_name__      )

       lowerCamelCase		:	Dict							=      self.image_processing_class.from_dict(
           self.image_processor_dict    ,  size=4_2    ,  max_size=8_4    ,  pad_and_return_pixel_mask=__magic_name__      )
       self.assertEqual(image_processor.size    ,  {"""shortest_edge""": 4_2, """longest_edge""": 8_4}      )
       self.assertEqual(image_processor.do_pad    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       pass
      def 						UpperCamelCase__							(      self      ):
       # Initialize image_processing
       lowerCamelCase		:	List[Any]							=      self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       lowerCamelCase		:	str							=      prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=__magic_name__      )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__    ,  Image.Image      )

       # Test not batched input
       lowerCamelCase		:	Union[str, Any]							=      image_processing(image_inputs[0]    ,  return_tensors="""pt"""      ).pixel_values

       lowerCamelCase			,    lowerCamelCase		:	Any							=      self.image_processor_tester.get_expected_values(__magic_name__      )

       self.assertEqual(
           encoded_images.shape    ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,  )

       # Test batched
       lowerCamelCase			,    lowerCamelCase		:	str							=      self.image_processor_tester.get_expected_values(__magic_name__    ,  batched=__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      image_processing(__magic_name__    ,  return_tensors="""pt"""      ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,  )
      def 						UpperCamelCase__							(      self      ):
       # Initialize image_processing
       lowerCamelCase		:	int							=      self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       lowerCamelCase		:	Union[str, Any]							=      prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=__magic_name__    ,  numpify=__magic_name__      )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__    ,  np.ndarray      )

       # Test not batched input
       lowerCamelCase		:	Optional[Any]							=      image_processing(image_inputs[0]    ,  return_tensors="""pt"""      ).pixel_values

       lowerCamelCase			,    lowerCamelCase		:	List[str]							=      self.image_processor_tester.get_expected_values(__magic_name__      )

       self.assertEqual(
           encoded_images.shape    ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,  )

       # Test batched
       lowerCamelCase		:	List[str]							=      image_processing(__magic_name__    ,  return_tensors="""pt"""      ).pixel_values

       lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      self.image_processor_tester.get_expected_values(__magic_name__    ,  batched=__magic_name__      )

       self.assertEqual(
           encoded_images.shape    ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,  )
      def 						UpperCamelCase__							(      self      ):
       # Initialize image_processing
       lowerCamelCase		:	List[str]							=      self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       lowerCamelCase		:	str							=      prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=__magic_name__    ,  torchify=__magic_name__      )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__    ,  torch.Tensor      )

       # Test not batched input
       lowerCamelCase		:	Union[str, Any]							=      image_processing(image_inputs[0]    ,  return_tensors="""pt"""      ).pixel_values

       lowerCamelCase			,    lowerCamelCase		:	Any							=      self.image_processor_tester.get_expected_values(__magic_name__      )

       self.assertEqual(
           encoded_images.shape    ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,  )

       # Test batched
       lowerCamelCase		:	int							=      image_processing(__magic_name__    ,  return_tensors="""pt"""      ).pixel_values

       lowerCamelCase			,    lowerCamelCase		:	str							=      self.image_processor_tester.get_expected_values(__magic_name__    ,  batched=__magic_name__      )

       self.assertEqual(
           encoded_images.shape    ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,  )
      def 						UpperCamelCase__							(      self      ):
       # Initialize image_processings
       lowerCamelCase		:	Tuple							=      self.image_processing_class(**self.image_processor_dict      )
       lowerCamelCase		:	str							=      self.image_processing_class(do_resize=__magic_name__    ,  do_normalize=__magic_name__    ,  do_rescale=__magic_name__      )
       # create random PyTorch tensors
       lowerCamelCase		:	int							=      prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=__magic_name__    ,  torchify=__magic_name__      )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__    ,  torch.Tensor      )

       # Test whether the method "pad" and calling the image processor return the same tensors
       lowerCamelCase		:	Dict							=      image_processing_a.pad(__magic_name__    ,  return_tensors="""pt"""      )
       lowerCamelCase		:	List[Any]							=      image_processing_a(__magic_name__    ,  return_tensors="""pt"""      )

       self.assertTrue(
           torch.allclose(encoded_images_with_method["""pixel_values"""]    ,  encoded_images["""pixel_values"""]    ,  atol=1e-4      )      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       # prepare image and target
       lowerCamelCase		:	str							=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,  """r"""      ) as f:
        lowerCamelCase		:	List[Any]							=      json.loads(f.read()      )

       lowerCamelCase		:	Optional[int]							=      {"""image_id""": 3_9_7_6_9, """annotations""": target}

       # encode them
       lowerCamelCase		:	str							=      YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""      )
       lowerCamelCase		:	Dict							=      image_processing(images=__magic_name__    ,  annotations=__magic_name__    ,  return_tensors="""pt"""      )

       # verify pixel values
       lowerCamelCase		:	int							=      torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
       self.assertEqual(encoding["""pixel_values"""].shape    ,  __magic_name__      )

       lowerCamelCase		:	Tuple							=      torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,  __magic_name__    ,  atol=1e-4      )      )

       # verify area
       lowerCamelCase		:	Union[str, Any]							=      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,  __magic_name__      )      )
       # verify boxes
       lowerCamelCase		:	Optional[Any]							=      torch.Size([6, 4]      )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,  __magic_name__      )
       lowerCamelCase		:	List[Any]							=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,  __magic_name__    ,  atol=1e-3      )      )
       # verify image_id
       lowerCamelCase		:	Union[str, Any]							=      torch.tensor([3_9_7_6_9]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,  __magic_name__      )      )
       # verify is_crowd
       lowerCamelCase		:	Optional[Any]							=      torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,  __magic_name__      )      )
       # verify class_labels
       lowerCamelCase		:	str							=      torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,  __magic_name__      )      )
       # verify orig_size
       lowerCamelCase		:	Union[str, Any]							=      torch.tensor([4_8_0, 6_4_0]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,  __magic_name__      )      )
       # verify size
       lowerCamelCase		:	Optional[int]							=      torch.tensor([8_0_0, 1_0_6_6]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,  __magic_name__      )      )






      @slow
      def 						UpperCamelCase__							(      self      ):
       # prepare image, target and masks_path
       lowerCamelCase		:	List[Any]							=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
       with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,  """r"""      ) as f:
        lowerCamelCase		:	str							=      json.loads(f.read()      )

       lowerCamelCase		:	List[str]							=      {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

       lowerCamelCase		:	List[Any]							=      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""      )

       # encode them
       lowerCamelCase		:	Tuple							=      YolosImageProcessor(format="""coco_panoptic"""      )
       lowerCamelCase		:	Tuple							=      image_processing(images=__magic_name__    ,  annotations=__magic_name__    ,  masks_path=__magic_name__    ,  return_tensors="""pt"""      )

       # verify pixel values
       lowerCamelCase		:	Optional[Any]							=      torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
       self.assertEqual(encoding["""pixel_values"""].shape    ,  __magic_name__      )

       lowerCamelCase		:	str							=      torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,  __magic_name__    ,  atol=1e-4      )      )

       # verify area
       lowerCamelCase		:	str							=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,  __magic_name__      )      )
       # verify boxes
       lowerCamelCase		:	Dict							=      torch.Size([6, 4]      )
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,  __magic_name__    ,  atol=1e-3      )      )
       # verify image_id
       lowerCamelCase		:	Union[str, Any]							=      torch.tensor([3_9_7_6_9]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,  __magic_name__      )      )
       # verify is_crowd
       lowerCamelCase		:	List[str]							=      torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,  __magic_name__      )      )
       # verify class_labels
       lowerCamelCase		:	List[Any]							=      torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,  __magic_name__      )      )
       # verify masks
       lowerCamelCase		:	Union[str, Any]							=      8_2_2_8_7_3
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,  __magic_name__      )
       # verify orig_size
       lowerCamelCase		:	List[Any]							=      torch.tensor([4_8_0, 6_4_0]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,  __magic_name__      )      )
       # verify size
       lowerCamelCase		:	Dict							=      torch.tensor([8_0_0, 1_0_6_6]      )
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,  __magic_name__      )      )






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower_vision_model"""
      def __init__(      self    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=3    ,  __magic_name__=1_6    ,  __magic_name__=2_8_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__=True    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	str							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	List[str]							=      patch_size
       lowerCamelCase		:	Tuple							=      image_size
       lowerCamelCase		:	Any							=      initializer_factor
       lowerCamelCase		:	Tuple							=      layer_norm_eps
       lowerCamelCase		:	Tuple							=      stop_gradient
       lowerCamelCase		:	Optional[int]							=      share_layernorm
       lowerCamelCase		:	str							=      remove_last_layer






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	str							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""bridgetower_text_model"""
      def __init__(      self    ,  __magic_name__=5_0_2_6_5    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=1    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_4    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	Any							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      hidden_act
       lowerCamelCase		:	Optional[int]							=      initializer_factor
       lowerCamelCase		:	Any							=      intermediate_size
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	Union[str, Any]							=      type_vocab_size
       lowerCamelCase		:	Optional[int]							=      layer_norm_eps
       lowerCamelCase		:	Optional[int]							=      position_embedding_type
       lowerCamelCase		:	List[str]							=      use_cache
       lowerCamelCase		:	List[str]							=      pad_token_id
       lowerCamelCase		:	List[str]							=      bos_token_id
       lowerCamelCase		:	Optional[int]							=      eos_token_id






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	Optional[int]							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower"""
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__="gelu"    ,  __magic_name__=7_6_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__="add"    ,  __magic_name__=1_2    ,  __magic_name__=6    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       # TODO: remove this once the Hub files are updated.
       lowerCamelCase		:	int							=      kwargs.pop("""text_config_dict"""    ,  __magic_name__      )
       lowerCamelCase		:	str							=      kwargs.pop("""vision_config_dict"""    ,  __magic_name__      )

       super().__init__(**__magic_name__      )
       lowerCamelCase		:	str							=      share_cross_modal_transformer_layers
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Tuple							=      initializer_factor
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	int							=      share_link_tower_layers
       lowerCamelCase		:	List[Any]							=      link_tower_type
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      tie_word_embeddings
       lowerCamelCase		:	Tuple							=      init_layernorm_from_vision_encoder

       if text_config is None:
        lowerCamelCase		:	Any							=      {}
        logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""      )

       if vision_config is None:
        lowerCamelCase		:	int							=      {}
        logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""      )

       lowerCamelCase		:	Any							=      BridgeTowerTextConfig(**__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      BridgeTowerVisionConfig(**__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       return cls(text_config=text_config.to_dict()    ,  vision_config=vision_config.to_dict()    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      copy.deepcopy(self.__dict__      )
       lowerCamelCase		:	int							=      self.text_config.to_dict()
       lowerCamelCase		:	Dict							=      self.vision_config.to_dict()
       lowerCamelCase		:	List[str]							=      self.__class__.model_type
       return output




