

"""simple docstring"""


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__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Tuple:
		__lowerCAmelCase:       Optional[int]          =			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
		__lowerCAmelCase:       List[str]          =			Image.open(requests.get(__lowerCamelCase					, stream=__lowerCamelCase ).raw ).convert("RGB" )

		__lowerCAmelCase:       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) ),
		    ] )
		__lowerCAmelCase:       Dict          =			transform(__lowerCamelCase ).unsqueeze(0 ).to(__lowerCamelCase )
		return image






def     a__    (    __SCREAMING_SNAKE_CASE )	->	List[Any]:
		if "visual_encoder" in key:
				__lowerCAmelCase:       Any          =			re.sub("visual_encoder*"					, "vision_model.encoder"					, __lowerCamelCase )
		if "blocks" in key:
				__lowerCAmelCase:       List[str]          =			re.sub(R"blocks"					, "layers"					, __lowerCamelCase )
		if "attn" in key:
				__lowerCAmelCase:       List[str]          =			re.sub(R"attn"					, "self_attn"					, __lowerCamelCase )
		if "norm1" in key:
				__lowerCAmelCase:       Dict          =			re.sub(R"norm1"					, "layer_norm1"					, __lowerCamelCase )
		if "norm2" in key:
				__lowerCAmelCase:       Optional[Any]          =			re.sub(R"norm2"					, "layer_norm2"					, __lowerCamelCase )
		if "encoder.norm" in key:
				__lowerCAmelCase:       Union[str, Any]          =			re.sub(R"encoder.norm"					, "post_layernorm"					, __lowerCamelCase )
		if "encoder.patch_embed.proj" in key:
				__lowerCAmelCase:       int          =			re.sub(R"encoder.patch_embed.proj"					, "embeddings.patch_embedding"					, __lowerCamelCase )

		if "encoder.pos_embed" in key:
				__lowerCAmelCase:       int          =			re.sub(R"encoder.pos_embed"					, "embeddings.position_embedding"					, __lowerCamelCase )
		if "encoder.cls_token" in key:
				__lowerCAmelCase:       Dict          =			re.sub(R"encoder.cls_token"					, "embeddings.class_embedding"					, __lowerCamelCase )

		if "self_attn" in key:
				__lowerCAmelCase:       Optional[int]          =			re.sub(R"self_attn.proj"					, "self_attn.projection"					, __lowerCamelCase )

		return key






@torch.no_grad()
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=None )	->	Optional[int]:
		if config_path is not None:
				__lowerCAmelCase:       Union[str, Any]          =			BlipConfig.from_pretrained(__lowerCamelCase )
		else:
				__lowerCAmelCase:       str          =			BlipConfig(projection_dim=5_1_2					, text_config={}					, vision_config={} )

		__lowerCAmelCase:       Dict          =			BlipForConditionalGeneration(__lowerCamelCase ).eval()

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

		__lowerCAmelCase:       Optional[int]          =			blip_decoder(pretrained=__lowerCamelCase					, image_size=3_8_4					, vit="base" )
		__lowerCAmelCase:       Optional[int]          =			pt_model.eval()

		__lowerCAmelCase:       Union[str, Any]          =			pt_model.state_dict()
		for key in modified_state_dict.copy():
				__lowerCAmelCase:       Any          =			modified_state_dict.pop(__lowerCamelCase )
				__lowerCAmelCase:       Union[str, Any]          =			rename_key(__lowerCamelCase )
				__lowerCAmelCase:       Union[str, Any]          =			value

		hf_model.load_state_dict(__lowerCamelCase )

		__lowerCAmelCase:       int          =			3_8_4
		__lowerCAmelCase:       Optional[int]          =			load_demo_image(image_size=__lowerCamelCase					, device="cpu" )
		__lowerCAmelCase:       str          =			BertTokenizer.from_pretrained("bert-base-uncased" )
		__lowerCAmelCase:       Dict          =			tokenizer(["a picture of"] ).input_ids

		__lowerCAmelCase:       Tuple          =			hf_model.generate(__lowerCamelCase					, __lowerCamelCase )

		assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

		__lowerCAmelCase:       Tuple          =			hf_model.generate(__lowerCamelCase )

		assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

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

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

		__lowerCAmelCase:       Optional[Any]          =			blip_vqa(pretrained=__lowerCamelCase					, image_size=__lowerCamelCase					, vit="base" )
		vqa_model.eval()

		__lowerCAmelCase:       Optional[Any]          =			vqa_model.state_dict()
		for key in modified_state_dict.copy():
				__lowerCAmelCase:       str          =			modified_state_dict.pop(__lowerCamelCase )
				__lowerCAmelCase:       Optional[Any]          =			rename_key(__lowerCamelCase )
				__lowerCAmelCase:       List[Any]          =			value

		__lowerCAmelCase:       Tuple          =			BlipForQuestionAnswering(__lowerCamelCase )

		hf_vqa_model.load_state_dict(__lowerCamelCase )

		__lowerCAmelCase:       Union[str, Any]          =			['''How many dogs are in this image?''']
		__lowerCAmelCase:       Tuple          =			tokenizer(__lowerCamelCase					, return_tensors="pt" ).input_ids

		__lowerCAmelCase:       Optional[int]          =			hf_vqa_model.generate(__lowerCamelCase					, __lowerCamelCase )
		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" )

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

		__lowerCAmelCase:       str          =			blip_itm(pretrained=__lowerCamelCase					, image_size=__lowerCamelCase					, vit="base" )
		itm_model.eval()

		__lowerCAmelCase:       str          =			itm_model.state_dict()
		for key in modified_state_dict.copy():
				__lowerCAmelCase:       str          =			modified_state_dict.pop(__lowerCamelCase )
				__lowerCAmelCase:       List[Any]          =			rename_key(__lowerCamelCase )
				__lowerCAmelCase:       Union[str, Any]          =			value

		__lowerCAmelCase:       Union[str, Any]          =			BlipForImageTextRetrieval(__lowerCamelCase )

		__lowerCAmelCase:       Any          =			['''A picture of a woman with a dog sitting in a beach''']
		__lowerCAmelCase:       Optional[Any]          =			tokenizer(
		    __lowerCamelCase					, return_tensors="pt"					, padding="max_length"					, truncation=__lowerCamelCase					, max_length=3_5					, ).input_ids

		hf_itm_model.load_state_dict(__lowerCamelCase )
		hf_itm_model.eval()

		__lowerCAmelCase:       Optional[Any]          =			hf_itm_model(__lowerCamelCase					, __lowerCamelCase					, use_itm_head=__lowerCamelCase )
		__lowerCAmelCase:       Any          =			hf_itm_model(__lowerCamelCase					, __lowerCamelCase					, use_itm_head=__lowerCamelCase )

		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__":
			__A						   =			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")
			__A						   =			parser.parse_args()

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







"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase_   						=   {
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				"tapas"







		def __init__(	self						:  List[Any]			,_snake_case						:  Dict=30_522			,_snake_case						:  Union[str, Any]=768			,_snake_case						:  int=12			,_snake_case						:  Union[str, Any]=12			,_snake_case						:  Union[str, Any]=3_072			,_snake_case						:  List[Any]="gelu"			,_snake_case						:  Optional[int]=0.1			,_snake_case						:  Tuple=0.1			,_snake_case						:  List[Any]=1_024			,_snake_case						:  Any=[3, 256, 256, 2, 256, 256, 10]			,_snake_case						:  List[Any]=0.02			,_snake_case						:  Union[str, Any]=1e-12			,_snake_case						:  str=0			,_snake_case						:  Any=10.0			,_snake_case						:  int=0			,_snake_case						:  Optional[Any]=1.0			,_snake_case						:  List[str]=None			,_snake_case						:  Tuple=1.0			,_snake_case						:  Tuple=False			,_snake_case						:  List[Any]=None			,_snake_case						:  int=1.0			,_snake_case						:  List[Any]=1.0			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]="ratio"			,_snake_case						:  Any=None			,_snake_case						:  Union[str, Any]=None			,_snake_case						:  List[str]=64			,_snake_case						:  Optional[Any]=32			,_snake_case						:  Optional[Any]=False			,_snake_case						:  Optional[int]=True			,_snake_case						:  Dict=False			,_snake_case						:  Tuple=False			,_snake_case						:  int=True			,_snake_case						:  List[str]=False			,_snake_case						:  Dict=None			,_snake_case						:  Optional[int]=None			,**_snake_case						:  int			,)					->		List[Any]:






				"""simple docstring"""



				super().__init__(pad_token_id=_snake_case			,**_snake_case						)

				# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
				lowercase__   :    Optional[int]				=			vocab_size
				lowercase__   :    List[str]				=			hidden_size
				lowercase__   :    Any				=			num_hidden_layers
				lowercase__   :    Optional[Any]				=			num_attention_heads
				lowercase__   :    Optional[int]				=			hidden_act
				lowercase__   :    List[Any]				=			intermediate_size
				lowercase__   :    List[Any]				=			hidden_dropout_prob
				lowercase__   :    Dict				=			attention_probs_dropout_prob
				lowercase__   :    str				=			max_position_embeddings
				lowercase__   :    Dict				=			type_vocab_sizes
				lowercase__   :    Optional[Any]				=			initializer_range
				lowercase__   :    Dict				=			layer_norm_eps

				# Fine-tuning task hyperparameters
				lowercase__   :    Any				=			positive_label_weight
				lowercase__   :    int				=			num_aggregation_labels
				lowercase__   :    List[str]				=			aggregation_loss_weight
				lowercase__   :    Optional[int]				=			use_answer_as_supervision
				lowercase__   :    Optional[Any]				=			answer_loss_importance
				lowercase__   :    Union[str, Any]				=			use_normalized_answer_loss
				lowercase__   :    str				=			huber_loss_delta
				lowercase__   :    str				=			temperature
				lowercase__   :    int				=			aggregation_temperature
				lowercase__   :    List[Any]				=			use_gumbel_for_cells
				lowercase__   :    Tuple				=			use_gumbel_for_aggregation
				lowercase__   :    Union[str, Any]				=			average_approximation_function
				lowercase__   :    Union[str, Any]				=			cell_selection_preference
				lowercase__   :    Any				=			answer_loss_cutoff
				lowercase__   :    List[Any]				=			max_num_rows
				lowercase__   :    str				=			max_num_columns
				lowercase__   :    int				=			average_logits_per_cell
				lowercase__   :    str				=			select_one_column
				lowercase__   :    str				=			allow_empty_column_selection
				lowercase__   :    Any				=			init_cell_selection_weights_to_zero
				lowercase__   :    Optional[int]				=			reset_position_index_per_cell
				lowercase__   :    Union[str, Any]				=			disable_per_token_loss

				# Aggregation hyperparameters
				lowercase__   :    Optional[Any]				=			aggregation_labels
				lowercase__   :    List[Any]				=			no_aggregation_label_index

				if isinstance(self.aggregation_labels			,_snake_case						):
						lowercase__   :    Union[str, Any]				=			{int(_snake_case						): v for k, v in aggregation_labels.items()}
