

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()
lowerCAmelCase__								=		logging.get_logger(__name__)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						SwinConfig(
	    embed_dim=1_9_2							,       depths=(2, 2, 1_8, 2)							,       num_heads=(6, 1_2, 2_4, 4_8)							,       window_size=1_2							,       out_features=['stage2', 'stage3', 'stage4']							,       )

	_UpperCamelCase   :		Optional[int]			=						DetaConfig(
	    backbone_config=UpperCAmelCase_							,       num_queries=9_0_0							,       encoder_ffn_dim=2_0_4_8							,       decoder_ffn_dim=2_0_4_8							,       num_feature_levels=5							,       assign_first_stage=UpperCAmelCase_							,       with_box_refine=UpperCAmelCase_							,       two_stage=UpperCAmelCase_							,       )

	# set labels
	_UpperCamelCase   :		Union[str, Any]			=						'huggingface/label-files'
	if "o365" in model_name:
		_UpperCamelCase   :		Tuple			=						3_6_6
		_UpperCamelCase   :		int			=						'object365-id2label.json'
	else:
		_UpperCamelCase   :		int			=						9_1
		_UpperCamelCase   :		Tuple			=						'coco-detection-id2label.json'

	_UpperCamelCase   :		int			=						num_labels
	_UpperCamelCase   :		Dict			=						json.load(open(cached_download(hf_hub_url(UpperCAmelCase_							,       UpperCAmelCase_							,       repo_type='dataset'	)	)							,       'r'	)	)
	_UpperCamelCase   :		Dict			=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
	_UpperCamelCase   :		Any			=						idalabel
	_UpperCamelCase   :		Union[str, Any]			=						{v: k for k, v in idalabel.items()}

	return config






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						[]

	# 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 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Dict	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						dct.pop(UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						val






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Tuple	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						[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   :		Tuple			=						state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''	)
			_UpperCamelCase   :		Optional[Any]			=						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
			_UpperCamelCase   :		List[str]			=						in_proj_weight[:dim, :]
			_UpperCamelCase   :		str			=						in_proj_bias[: dim]
			_UpperCamelCase   :		List[str]			=						in_proj_weight[
			    dim : dim * 2, :
			]
			_UpperCamelCase   :		Union[str, Any]			=						in_proj_bias[
			    dim : dim * 2
			]
			_UpperCamelCase   :		Any			=						in_proj_weight[
			    -dim :, :
			]
			_UpperCamelCase   :		Tuple			=						in_proj_bias[-dim :]
			# fmt: on






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Any	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		List[str]			=						config.d_model
	for i in range(config.decoder_layers	):
		# read in weights + bias of input projection layer of self-attention
		_UpperCamelCase   :		Union[str, Any]			=						state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
		_UpperCamelCase   :		Optional[Any]			=						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
		_UpperCamelCase   :		Any			=						in_proj_weight[:hidden_size, :]
		_UpperCamelCase   :		str			=						in_proj_bias[:hidden_size]
		_UpperCamelCase   :		str			=						in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		_UpperCamelCase   :		List[str]			=						in_proj_bias[hidden_size : hidden_size * 2]
		_UpperCamelCase   :		str			=						in_proj_weight[-hidden_size:, :]
		_UpperCamelCase   :		Optional[int]			=						in_proj_bias[-hidden_size:]






def 					lowerCamelCase_     (							)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						'http://images.cocodataset.org/val2017/000000039769.jpg'
	_UpperCamelCase   :		str			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	)

	return im






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     List[str]	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						get_deta_config(UpperCAmelCase_	)

	# load original state dict
	if model_name == "deta-swin-large":
		_UpperCamelCase   :		int			=						hf_hub_download(repo_id='nielsr/deta-checkpoints'							,       filename='adet_swin_ft.pth'	)
	elif model_name == "deta-swin-large-o365":
		_UpperCamelCase   :		str			=						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'''	)

	_UpperCamelCase   :		str			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)['model']

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

	# rename keys
	_UpperCamelCase   :		Union[str, Any]			=						create_rename_keys(UpperCAmelCase_	)
	for src, dest in rename_keys:
		rename_key(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	read_in_swin_q_k_v(UpperCAmelCase_							,       config.backbone_config	)
	read_in_decoder_q_k_v(UpperCAmelCase_							,       UpperCAmelCase_	)

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			_UpperCamelCase   :		str			=						state_dict.pop(UpperCAmelCase_	)
			_UpperCamelCase   :		List[str]			=						val
		if "input_proj" in key:
			_UpperCamelCase   :		int			=						state_dict.pop(UpperCAmelCase_	)
			_UpperCamelCase   :		List[Any]			=						val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			_UpperCamelCase   :		Union[str, Any]			=						state_dict.pop(UpperCAmelCase_	)
			_UpperCamelCase   :		Any			=						val

    # finally, create HuggingFace model and load state dict
	_UpperCamelCase   :		Any			=						DetaForObjectDetection(UpperCAmelCase_	)
	model.load_state_dict(UpperCAmelCase_	)
	model.eval()

	_UpperCamelCase   :		List[str]			=						'cuda' if torch.cuda.is_available() else 'cpu'
	model.to(UpperCAmelCase_	)

	# load image processor
	_UpperCamelCase   :		Any			=						DetaImageProcessor(format='coco_detection'	)

	# verify our conversion on image
	_UpperCamelCase   :		int			=						prepare_img()
	_UpperCamelCase   :		List[str]			=						processor(images=UpperCAmelCase_							,       return_tensors='pt'	)
	_UpperCamelCase   :		List[str]			=						encoding['pixel_values']
	_UpperCamelCase   :		Optional[Any]			=						model(pixel_values.to(UpperCAmelCase_	)	)

	# verify logits
	print('Logits:'							,       outputs.logits[0, :3, :3]	)
	print('Boxes:'							,       outputs.pred_boxes[0, :3, :3]	)
	if model_name == "deta-swin-large":
		_UpperCamelCase   :		Union[str, Any]			=						torch.tensor(
		    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]	)
		_UpperCamelCase   :		Optional[Any]			=						torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]	)
	elif model_name == "deta-swin-large-o365":
		_UpperCamelCase   :		Dict			=						torch.tensor(
		    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]	)
		_UpperCamelCase   :		Optional[int]			=						torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]	)
	assert torch.allclose(outputs.logits[0, :3, :3]							,       expected_logits.to(UpperCAmelCase_	)							,       atol=1e-4	)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,       expected_boxes.to(UpperCAmelCase_	)							,       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(UpperCAmelCase_	).mkdir(exist_ok=UpperCAmelCase_	)
		model.save_pretrained(UpperCAmelCase_	)
		processor.save_pretrained(UpperCAmelCase_	)

	# 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__":
							lowerCAmelCase__								=		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."""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__								=		{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

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


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlipModel""",
							    """BlipPreTrainedModel""",
							    """BlipForConditionalGeneration""",
							    """BlipForQuestionAnswering""",
							    """BlipVisionModel""",
							    """BlipTextModel""",
							    """BlipForImageTextRetrieval""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBlipModel""",
							    """TFBlipPreTrainedModel""",
							    """TFBlipForConditionalGeneration""",
							    """TFBlipForQuestionAnswering""",
							    """TFBlipVisionModel""",
							    """TFBlipTextModel""",
							    """TFBlipForImageTextRetrieval""",
							]

if TYPE_CHECKING:
							from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
							from .processing_blip import BlipProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_blip import BlipImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blip import (
														    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlipForConditionalGeneration,
														    BlipForImageTextRetrieval,
														    BlipForQuestionAnswering,
														    BlipModel,
														    BlipPreTrainedModel,
														    BlipTextModel,
														    BlipVisionModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blip import (
														    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBlipForConditionalGeneration,
														    TFBlipForImageTextRetrieval,
														    TFBlipForQuestionAnswering,
														    TFBlipModel,
														    TFBlipPreTrainedModel,
														    TFBlipTextModel,
														    TFBlipVisionModel,
														)

else:
							import sys

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




