from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCamelCase		:       str									=     logging.get_logger(__name__)

__UpperCamelCase		:       str									=     {
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_):
						UpperCamelCase_     =		"""swin"""

						UpperCamelCase_     =		{
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}


						def __init__(       self	:				str    ,		UpperCamelCase__	:				List[str]=224    ,		UpperCamelCase__	:				int=4    ,		UpperCamelCase__	:				Optional[int]=3    ,		UpperCamelCase__	:				Tuple=96    ,		UpperCamelCase__	:				Optional[int]=[2, 2, 6, 2]    ,		UpperCamelCase__	:				Union[str, Any]=[3, 6, 12, 24]    ,		UpperCamelCase__	:				str=7    ,		UpperCamelCase__	:				str=4.0    ,		UpperCamelCase__	:				Any=True    ,		UpperCamelCase__	:				List[Any]=0.0    ,		UpperCamelCase__	:				List[str]=0.0    ,		UpperCamelCase__	:				Tuple=0.1    ,		UpperCamelCase__	:				Union[str, Any]="gelu"    ,		UpperCamelCase__	:				Union[str, Any]=False    ,		UpperCamelCase__	:				Union[str, Any]=0.02    ,		UpperCamelCase__	:				List[Any]=1E-5    ,		UpperCamelCase__	:				Optional[int]=32    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				Optional[Any]=None    ,		**UpperCamelCase__	:				Any    ,		):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							patch_size
												SCREAMING_SNAKE_CASE							:    Tuple							=							num_channels
												SCREAMING_SNAKE_CASE							:    Dict							=							embed_dim
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							depths
												SCREAMING_SNAKE_CASE							:    List[str]							=							len(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							num_heads
												SCREAMING_SNAKE_CASE							:    Tuple							=							window_size
												SCREAMING_SNAKE_CASE							:    int							=							mlp_ratio
												SCREAMING_SNAKE_CASE							:    Any							=							qkv_bias
												SCREAMING_SNAKE_CASE							:    str							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    int							=							drop_path_rate
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							use_absolute_embeddings
												SCREAMING_SNAKE_CASE							:    int							=							layer_norm_eps
												SCREAMING_SNAKE_CASE							:    List[str]							=							initializer_range
												SCREAMING_SNAKE_CASE							:    int							=							encoder_stride
												# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							int(embed_dim * 2 ** (len(UpperCamelCase__						) - 1)						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							['''stem'''] + [f"""stage{idx}""" for idx in range(1    ,		len(UpperCamelCase__						) + 1						)]
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							get_aligned_output_features_output_indices(
												    out_features=UpperCamelCase__    ,		out_indices=UpperCamelCase__    ,		stage_names=self.stage_names						)






class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		version.parse("""1.11""")


						@property
						def        __A       (       self	:				str						):





												'''simple docstring'''





												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ]						)



						@property
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												return 1E-4






import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Image.open(requests.get(_lowercase		,  stream=_lowercase						).raw						).convert('''RGB'''						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)		,  interpolation=InterpolationMode.BICUBIC						),
						        transforms.ToTensor(),
						        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)		,  (0.2686_2954, 0.2613_0258, 0.2757_7711)						),
						    ]						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							transform(_lowercase						).unsqueeze(0						).to(_lowercase						)
						return image
def       A	(    _lowercase						):
						if "visual_encoder" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub('''visual_encoder*'''		,  '''vision_model.encoder'''		,  _lowercase						)
						if "blocks" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''blocks'''		,  '''layers'''		,  _lowercase						)
						if "attn" in key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							re.sub(R'''attn'''		,  '''self_attn'''		,  _lowercase						)
						if "norm1" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''norm1'''		,  '''layer_norm1'''		,  _lowercase						)
						if "norm2" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''norm2'''		,  '''layer_norm2'''		,  _lowercase						)
						if "encoder.norm" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub(R'''encoder.norm'''		,  '''post_layernorm'''		,  _lowercase						)
						if "encoder.patch_embed.proj" in key:
												SCREAMING_SNAKE_CASE							:    Dict							=							re.sub(R'''encoder.patch_embed.proj'''		,  '''embeddings.patch_embedding'''		,  _lowercase						)

						if "encoder.pos_embed" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''encoder.pos_embed'''		,  '''embeddings.position_embedding'''		,  _lowercase						)
						if "encoder.cls_token" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''encoder.cls_token'''		,  '''embeddings.class_embedding'''		,  _lowercase						)

						if "self_attn" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''self_attn.proj'''		,  '''self_attn.projection'''		,  _lowercase						)

						return key
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase=None						):
						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Any							=							BlipConfig.from_pretrained(_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							BlipConfig(projection_dim=512		,  text_config={}		,  vision_config={}						)

						SCREAMING_SNAKE_CASE							:    str							=							BlipForConditionalGeneration(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_decoder(pretrained=_lowercase		,  image_size=384		,  vit='''base'''						)
						SCREAMING_SNAKE_CASE							:    Any							=							pt_model.eval()

						SCREAMING_SNAKE_CASE							:    Dict							=							pt_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Tuple							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Any							=							value

						hf_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							384
						SCREAMING_SNAKE_CASE							:    Tuple							=							load_demo_image(image_size=_lowercase		,  device='''cpu'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							BertTokenizer.from_pretrained('''bert-base-uncased'''						)
						SCREAMING_SNAKE_CASE							:    int							=							tokenizer(['''a picture of''']						).input_ids

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							hf_model.generate(_lowercase		,  _lowercase						)

						assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_model.generate(_lowercase						)

						assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

						if pytorch_dump_folder_path is not None:
												hf_model.save_pretrained(_lowercase						)

						# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
						SCREAMING_SNAKE_CASE							:    str							=							(
						    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_vqa(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						vqa_model.eval()

						SCREAMING_SNAKE_CASE							:    List[str]							=							vqa_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							value

						SCREAMING_SNAKE_CASE							:    Dict							=							BlipForQuestionAnswering(_lowercase						)

						hf_vqa_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Dict							=							['''How many dogs are in this image?''']
						SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(_lowercase		,  return_tensors='''pt'''						).input_ids

						SCREAMING_SNAKE_CASE							:    Dict							=							hf_vqa_model.generate(_lowercase		,  _lowercase						)
						print(tokenizer.decode(answer[0]						)						)

						assert tokenizer.decode(answer[0]						) == "[UNK] 1 [SEP]"
						if pytorch_dump_folder_path is not None:
												hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''						)

						SCREAMING_SNAKE_CASE							:    Any							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

						SCREAMING_SNAKE_CASE							:    int							=							blip_itm(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						itm_model.eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							itm_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    int							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Dict							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							value

						SCREAMING_SNAKE_CASE							:    int							=							BlipForImageTextRetrieval(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							['''A picture of a woman with a dog sitting in a beach''']
						SCREAMING_SNAKE_CASE							:    str							=							tokenizer(
						    _lowercase		,  return_tensors='''pt'''		,  padding='''max_length'''		,  truncation=_lowercase		,  max_length=35		,  ).input_ids

						hf_itm_model.load_state_dict(_lowercase						)
						hf_itm_model.eval()

						SCREAMING_SNAKE_CASE							:    int							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)

						assert out[0].item() == 0.2110_6874_9427_7954
						assert torch.nn.functional.softmax(out_itm[0]		,  dim=1						)[:, 1].item() == 0.4_5698_8453_8650_5127

						if pytorch_dump_folder_path is not None:
												hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''						)


if __name__ == "__main__":
					__UpperCamelCase		:       Optional[int]									=     argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					__UpperCamelCase		:       int									=     parser.parse_args()

					convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





