import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)






def  snake_case  (       snake_case__			:Tuple)						->   List[str]:
       _A        =  SwinConfig(
           embed_dim=192   ,	depths=(2, 2, 18, 2)   ,	num_heads=(6, 12, 24, 48)   ,	window_size=12   ,	out_features=["""stage2""", """stage3""", """stage4"""]   ,	)

       _A        =  DetaConfig(
           backbone_config=snake_case__   ,	num_queries=900   ,	encoder_ffn_dim=2_048   ,	decoder_ffn_dim=2_048   ,	num_feature_levels=5   ,	assign_first_stage=snake_case__   ,	with_box_refine=snake_case__   ,	two_stage=snake_case__   ,	)

       # set labels
       _A        =  """huggingface/label-files"""
       if "o365" in model_name:
              _A        =  366
              _A        =  """object365-id2label.json"""
       else:
              _A        =  91
              _A        =  """coco-detection-id2label.json"""

       _A        =  num_labels
       _A        =  json.load(open(cached_download(hf_hub_url(snake_case__   ,	snake_case__   ,	repo_type="""dataset"""))   ,	"""r"""))
       _A        =  {int(snake_case__): v for k, v in idalabel.items()}
       _A        =  idalabel
       _A        =  {v: k for k, v in idalabel.items()}

       return config






def  snake_case  (       snake_case__			:Tuple)						->   Any:
       _A        =  []

       # stem
       # fmt: off
       rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight"""))
       rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias"""))
       rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight"""))
       rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias"""))
       # stages
       for i in range(len(config.backbone_config.depths)):
              for j in range(config.backbone_config.depths[i]):
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias'''))

              if i < 3:
                     rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight'''))
                     rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias'''))

       rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight"""))
       rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias"""))
       rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight"""))
       rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias"""))
       rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight"""))
       rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias"""))

       # transformer encoder
       for i in range(config.encoder_layers):
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight'''))
              rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias'''))

       # transformer decoder
       for i in range(config.decoder_layers):
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight'''))
              rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias'''))

       # fmt: on

       return rename_keys






def  snake_case  (       snake_case__			:Any   ,	snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Any:
       _A        =  dct.pop(snake_case__)
       _A        =  val






def  snake_case  (       snake_case__			:int   ,	snake_case__			:Union[str, Any])						->   Tuple:
       _A        =  [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
       for i in range(len(backbone_config.depths)):
              _A        =  num_features[i]
              for j in range(backbone_config.depths[i]):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     _A        =  state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight''')
                     _A        =  state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias''')
                     # next, add query, keys and values (in that order) to the state dict
                     _A        =  in_proj_weight[:dim, :]
                     _A        =  in_proj_bias[: dim]
                     _A        =  in_proj_weight[
                         dim : dim * 2, :
                     ]
                     _A        =  in_proj_bias[
                         dim : dim * 2
                     ]
                     _A        =  in_proj_weight[
                         -dim :, :
                     ]
                     _A        =  in_proj_bias[-dim :]
                     # fmt: on






def  snake_case  (       snake_case__			:List[Any]   ,	snake_case__			:int)						->   Optional[int]:
       # transformer decoder self-attention layers
       _A        =  config.d_model
       for i in range(config.decoder_layers):
              # read in weights + bias of input projection layer of self-attention
              _A        =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
              _A        =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
              # next, add query, keys and values (in that order) to the state dict
              _A        =  in_proj_weight[:hidden_size, :]
              _A        =  in_proj_bias[:hidden_size]
              _A        =  in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              _A        =  in_proj_bias[hidden_size : hidden_size * 2]
              _A        =  in_proj_weight[-hidden_size:, :]
              _A        =  in_proj_bias[-hidden_size:]






def  snake_case  (       )						->   Union[str, Any]:
       _A        =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _A        =  Image.open(requests.get(snake_case__   ,	stream=snake_case__).raw)

       return im






@torch.no_grad()
def  snake_case  (       snake_case__			:Dict   ,	snake_case__			:Tuple   ,	snake_case__			:Optional[int])						->   Union[str, Any]:
       _A        =  get_deta_config(snake_case__)

       # load original state dict
       if model_name == "deta-swin-large":
              _A        =  hf_hub_download(repo_id="""nielsr/deta-checkpoints"""   ,	filename="""adet_swin_ft.pth""")
       elif model_name == "deta-swin-large-o365":
              _A        =  hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""   ,	filename="""deta_swin_pt_o365.pth""")
       else:
              raise ValueError(F'''Model name {model_name} not supported''')

       _A        =  torch.load(snake_case__   ,	map_location="""cpu""")["""model"""]

       # original state dict
       for name, param in state_dict.items():
              print(snake_case__   ,	param.shape)

       # rename keys
       _A        =  create_rename_keys(snake_case__)
       for src, dest in rename_keys:
              rename_key(snake_case__   ,	snake_case__   ,	snake_case__)
       read_in_swin_q_k_v(snake_case__   ,	config.backbone_config)
       read_in_decoder_q_k_v(snake_case__   ,	snake_case__)

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     _A        =  state_dict.pop(snake_case__)
                     _A        =  val
              if "input_proj" in key:
                     _A        =  state_dict.pop(snake_case__)
                     _A        =  val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     _A        =  state_dict.pop(snake_case__)
                     _A        =  val

    # finally, create HuggingFace model and load state dict
       _A        =  DetaForObjectDetection(snake_case__)
       model.load_state_dict(snake_case__)
       model.eval()

       _A        =  """cuda""" if torch.cuda.is_available() else """cpu"""
       model.to(snake_case__)

       # load image processor
       _A        =  DetaImageProcessor(format="""coco_detection""")

       # verify our conversion on image
       _A        =  prepare_img()
       _A        =  processor(images=snake_case__   ,	return_tensors="""pt""")
       _A        =  encoding["""pixel_values"""]
       _A        =  model(pixel_values.to(snake_case__))

       # verify logits
       print("""Logits:"""   ,	outputs.logits[0, :3, :3])
       print("""Boxes:"""   ,	outputs.pred_boxes[0, :3, :3])
       if model_name == "deta-swin-large":
              _A        =  torch.tensor(
                  [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]])
              _A        =  torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]])
       elif model_name == "deta-swin-large-o365":
              _A        =  torch.tensor(
                  [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]])
              _A        =  torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]])
       assert torch.allclose(outputs.logits[0, :3, :3]   ,	expected_logits.to(snake_case__)   ,	atol=1E-4)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,	expected_boxes.to(snake_case__)   ,	atol=1E-4)
       print("""Everything ok!""")

       if pytorch_dump_folder_path:
              # Save model and processor
              logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...''')
              Path(snake_case__).mkdir(exist_ok=snake_case__)
              model.save_pretrained(snake_case__)
              processor.save_pretrained(snake_case__)

       # Push to hub
       if push_to_hub:
              print("""Pushing model and processor to hub...""")
              model.push_to_hub(F'''jozhang97/{model_name}''')
              processor.push_to_hub(F'''jozhang97/{model_name}''')


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        type=str,
        default='deta-swin-large',
        choices=['deta-swin-large', 'deta-swin-large-o365'],
        help='Name of the model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        help='Path to the folder to output PyTorch model.',
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import copy
import os
from typing import Union

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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    '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							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		'''bridgetower_vision_model'''



 def __init__(					self		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=12		,				lowerCAmelCase_=3		,				lowerCAmelCase_=16		,				lowerCAmelCase_=2_88		,				lowerCAmelCase_=1		,				lowerCAmelCase_=1E-05		,				lowerCAmelCase_=False		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				**lowerCAmelCase_		,				)  ->					Any:
        super().__init__(**lowerCAmelCase_							)
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_channels
        _A        =  patch_size
        _A        =  image_size
        _A        =  initializer_factor
        _A        =  layer_norm_eps
        _A        =  stop_gradient
        _A        =  share_layernorm
        _A        =  remove_last_layer



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					"PretrainedConfig":
        _A   ,  _A        =  cls.get_config_dict(lowerCAmelCase_		,				**lowerCAmelCase_							)

        if config_dict.get("""model_type"""							) == "bridgetower":
               _A        =  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(lowerCAmelCase_		,				**lowerCAmelCase_							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		'''bridgetower_text_model'''



 def __init__(					self		,				lowerCAmelCase_=5_02_65		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=12		,				lowerCAmelCase_=12		,				lowerCAmelCase_=1		,				lowerCAmelCase_=30_72		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=5_14		,				lowerCAmelCase_=1		,				lowerCAmelCase_=1E-05		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				lowerCAmelCase_="absolute"		,				lowerCAmelCase_=True		,				**lowerCAmelCase_		,				)  ->					Optional[Any]:
        super().__init__(**lowerCAmelCase_							)

        _A        =  vocab_size
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  hidden_act
        _A        =  initializer_factor
        _A        =  intermediate_size
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  max_position_embeddings
        _A        =  type_vocab_size
        _A        =  layer_norm_eps
        _A        =  position_embedding_type
        _A        =  use_cache
        _A        =  pad_token_id
        _A        =  bos_token_id
        _A        =  eos_token_id



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					"PretrainedConfig":
        _A   ,  _A        =  cls.get_config_dict(lowerCAmelCase_		,				**lowerCAmelCase_							)

        if config_dict.get("""model_type"""							) == "bridgetower":
               _A        =  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(lowerCAmelCase_		,				**lowerCAmelCase_							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[int]    			=		'''bridgetower'''



 def __init__(					self		,				lowerCAmelCase_=True		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=1		,				lowerCAmelCase_=1E-05		,				lowerCAmelCase_=False		,				lowerCAmelCase_="add"		,				lowerCAmelCase_=12		,				lowerCAmelCase_=6		,				lowerCAmelCase_=False		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					int:
        # TODO: remove this once the Hub files are updated.
        _A        =  kwargs.pop("""text_config_dict"""		,				lowerCAmelCase_							)
        _A        =  kwargs.pop("""vision_config_dict"""		,				lowerCAmelCase_							)

        super().__init__(**lowerCAmelCase_							)
        _A        =  share_cross_modal_transformer_layers
        _A        =  hidden_act
        _A        =  hidden_size
        _A        =  initializer_factor
        _A        =  layer_norm_eps
        _A        =  share_link_tower_layers
        _A        =  link_tower_type
        _A        =  num_attention_heads
        _A        =  num_hidden_layers
        _A        =  tie_word_embeddings
        _A        =  init_layernorm_from_vision_encoder

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

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

        _A        =  BridgeTowerTextConfig(**lowerCAmelCase_							)
        _A        =  BridgeTowerVisionConfig(**lowerCAmelCase_							)



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Any:
        return cls(text_config=text_config.to_dict()		,				vision_config=vision_config.to_dict()		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  copy.deepcopy(self.__dict__							)
        _A        =  self.text_config.to_dict()
        _A        =  self.vision_config.to_dict()
        _A        =  self.__class__.model_type
        return output




