import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:     Tuple         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Optional[Any]  =		SwinConfig.from_pretrained(
						    """microsoft/swin-tiny-patch4-window7-224"""		, out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			)
						UpperCamelCase_		:							Any  =		MaskFormerConfig(backbone_config=_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Any  =		"""huggingface/label-files"""
						if "ade20k-full" in model_name:
												# this should be ok
												UpperCamelCase_		:							Optional[int]  =		847
												UpperCamelCase_		:							List[str]  =		"""maskformer-ade20k-full-id2label.json"""
						elif "ade" in model_name:
												# this should be ok
												UpperCamelCase_		:							List[str]  =		150
												UpperCamelCase_		:							Optional[int]  =		"""ade20k-id2label.json"""
						elif "coco-stuff" in model_name:
												# this should be ok
												UpperCamelCase_		:							List[Any]  =		171
												UpperCamelCase_		:							int  =		"""maskformer-coco-stuff-id2label.json"""
						elif "coco" in model_name:
												# TODO
												UpperCamelCase_		:							Optional[int]  =		133
												UpperCamelCase_		:							Dict  =		"""coco-panoptic-id2label.json"""
						elif "cityscapes" in model_name:
												# this should be ok
												UpperCamelCase_		:							Union[str, Any]  =		19
												UpperCamelCase_		:							str  =		"""cityscapes-id2label.json"""
						elif "vistas" in model_name:
												# this should be ok
												UpperCamelCase_		:							List[Any]  =		65
												UpperCamelCase_		:							Dict  =		"""mapillary-vistas-id2label.json"""

						UpperCamelCase_		:							Tuple  =		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, repo_type="""dataset"""			)		, """r"""			)			)
						UpperCamelCase_		:							List[str]  =		{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}

						return config






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict			):
						UpperCamelCase_		:							Optional[int]  =		[]
						# stem
						# fmt: off
						rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")			)
						rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")			)
						rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")			)
						rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

												if i < 3:
																		rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')			)
																		rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')			)
												rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')			)
												rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')			)

						# FPN
						rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")			)
						rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")			)
						rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")			)
						for source_index, target_index in zip(range(3		, 0		, -1			)		, range(0		, 3			)			):
												rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')			)
												rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')			)
												rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')			)
												rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')			)
												rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')			)
												rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')			)
						rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")			)
						rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")			)

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers			):
												# self-attention out projection
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')			)
												# cross-attention out projection
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')			)
												# MLP 1
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')			)
												# MLP 2
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')			)
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')			)
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')			)
												# layernorm 3 (final layernorm)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')			)

						rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")			)
						rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")			)

						# heads on top
						rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")			)

						rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")			)
						rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")			)

						rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")			)
						rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")			)

						for i in range(3			):
												rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''')			)
												rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''')			)
						# fmt: on

						return rename_keys






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Any			):
						UpperCamelCase_		:							Any  =		dct.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							int  =		val






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Optional[int]			):
						UpperCamelCase_		:							Union[str, Any]  =		[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
						for i in range(len(backbone_config.depths			)			):
												UpperCamelCase_		:							Union[str, Any]  =		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)
																		UpperCamelCase_		:							List[str]  =		state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
																		UpperCamelCase_		:							Optional[int]  =		state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''			)
																		# next, add query, keys and values (in that order) to the state dict
																		UpperCamelCase_		:							Optional[Any]  =		in_proj_weight[:dim, :]
																		UpperCamelCase_		:							Tuple  =		in_proj_bias[: dim]
																		UpperCamelCase_		:							Tuple  =		in_proj_weight[
																		    dim : dim * 2, :
																		]
																		UpperCamelCase_		:							Any  =		in_proj_bias[
																		    dim : dim * 2
																		]
																		UpperCamelCase_		:							str  =		in_proj_weight[
																		    -dim :, :
																		]
																		UpperCamelCase_		:							Dict  =		in_proj_bias[-dim :]
																		# fmt: on






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Dict			):
						# fmt: off
						UpperCamelCase_		:							Dict  =		config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers			):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												UpperCamelCase_		:							int  =		state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
												UpperCamelCase_		:							Any  =		state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''			)
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase_		:							Dict  =		in_proj_weight[: hidden_size, :]
												UpperCamelCase_		:							Optional[Any]  =		in_proj_bias[:config.hidden_size]
												UpperCamelCase_		:							str  =		in_proj_weight[hidden_size : hidden_size * 2, :]
												UpperCamelCase_		:							int  =		in_proj_bias[hidden_size : hidden_size * 2]
												UpperCamelCase_		:							Dict  =		in_proj_weight[-hidden_size :, :]
												UpperCamelCase_		:							Optional[Any]  =		in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												UpperCamelCase_		:							int  =		state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
												UpperCamelCase_		:							Dict  =		state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''			)
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase_		:							int  =		in_proj_weight[: hidden_size, :]
												UpperCamelCase_		:							Optional[int]  =		in_proj_bias[:config.hidden_size]
												UpperCamelCase_		:							Optional[Any]  =		in_proj_weight[hidden_size : hidden_size * 2, :]
												UpperCamelCase_		:							Optional[int]  =		in_proj_bias[hidden_size : hidden_size * 2]
												UpperCamelCase_		:							Dict  =		in_proj_weight[-hidden_size :, :]
												UpperCamelCase_		:							Any  =		in_proj_bias[-hidden_size :]
						# fmt: on






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							List[str]  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase_		:							List[Any]  =		Image.open(requests.get(_SCREAMING_SNAKE_CASE		, stream=_SCREAMING_SNAKE_CASE			).raw			)
						return im






@torch.no_grad()
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				bool = False			):
						UpperCamelCase_		:							List[Any]  =		get_maskformer_config(_SCREAMING_SNAKE_CASE			)

						# load original state_dict
						with open(_SCREAMING_SNAKE_CASE		, """rb"""			) as f:
												UpperCamelCase_		:							Optional[int]  =		pickle.load(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[Any]  =		data["""model"""]

						# for name, param in state_dict.items():
						#     print(name, param.shape)

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

						# update to torch tensors
						for key, value in state_dict.items():
												UpperCamelCase_		:							str  =		torch.from_numpy(_SCREAMING_SNAKE_CASE			)

						# load 🤗 model
						UpperCamelCase_		:							Optional[int]  =		MaskFormerForInstanceSegmentation(_SCREAMING_SNAKE_CASE			)
						model.eval()

						for name, param in model.named_parameters():
												print(_SCREAMING_SNAKE_CASE		, param.shape			)

						UpperCamelCase_,UpperCamelCase_		:							Tuple  =		model.load_state_dict(_SCREAMING_SNAKE_CASE		, strict=_SCREAMING_SNAKE_CASE			)
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(_SCREAMING_SNAKE_CASE			) == 0, f'''Unexpected keys: {unexpected_keys}'''

						# verify results
						UpperCamelCase_		:							List[str]  =		prepare_img()
						if "vistas" in model_name:
												UpperCamelCase_		:							Optional[int]  =		65
						elif "cityscapes" in model_name:
												UpperCamelCase_		:							Union[str, Any]  =		6_5535
						else:
												UpperCamelCase_		:							List[Any]  =		255
						UpperCamelCase_		:							Dict  =		True if """ade""" in model_name else False
						UpperCamelCase_		:							Any  =		MaskFormerImageProcessor(ignore_index=_SCREAMING_SNAKE_CASE		, reduce_labels=_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Tuple  =		image_processor(_SCREAMING_SNAKE_CASE		, return_tensors="""pt"""			)

						UpperCamelCase_		:							Optional[Any]  =		model(**_SCREAMING_SNAKE_CASE			)

						print("""Logits:"""		, outputs.class_queries_logits[0, :3, :3]			)

						if model_name == "maskformer-swin-tiny-ade":
												UpperCamelCase_		:							List[Any]  =		torch.tensor(
												    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]			)
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=1E-4			)
						print("""Looks ok!"""			)

						if pytorch_dump_folder_path is not None:
												print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''			)
												Path(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
												model.save_pretrained(_SCREAMING_SNAKE_CASE			)
												image_processor.save_pretrained(_SCREAMING_SNAKE_CASE			)

						if push_to_hub:
												print("""Pushing model and image processor to the hub..."""			)
												model.push_to_hub(f'''nielsr/{model_name}'''			)
												image_processor.push_to_hub(f'''nielsr/{model_name}'''			)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE		:     List[Any]         =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="maskformer-swin-tiny-ade",
	    type=str,
	    help=("Name of the MaskFormer model you'd like to convert",),
	)
	parser.add_argument(
	    "--checkpoint_path",
	    default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
	    type=str,
	    help="Path to the original state dict (.pth file).",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	SCREAMING_SNAKE_CASE		:     List[str]         =	parser.parse_args()
	convert_maskformer_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






