





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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							List[str]    ):
		SCREAMING_SNAKE_CASE__			=      "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(lowerCAmelCase_					,   stream=lowerCAmelCase_    ).raw    ).convert("""RGB"""    )

		SCREAMING_SNAKE_CASE__			=      transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size)					,   interpolation=InterpolationMode.BICUBIC    ),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)					,   (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)    ),
		    ]    )
		SCREAMING_SNAKE_CASE__			=      transform(lowerCAmelCase_    ).unsqueeze(0    ).to(lowerCAmelCase_    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):


		if "visual_encoder" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub("""visual_encoder*"""					,   """vision_model.encoder"""					,   lowerCAmelCase_    )
		if "blocks" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""blocks"""					,   """layers"""					,   lowerCAmelCase_    )
		if "attn" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""attn"""					,   """self_attn"""					,   lowerCAmelCase_    )
		if "norm1" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""norm1"""					,   """layer_norm1"""					,   lowerCAmelCase_    )
		if "norm2" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""norm2"""					,   """layer_norm2"""					,   lowerCAmelCase_    )
		if "encoder.norm" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""encoder.norm"""					,   """post_layernorm"""					,   lowerCAmelCase_    )
		if "encoder.patch_embed.proj" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""encoder.patch_embed.proj"""					,   """embeddings.patch_embedding"""					,   lowerCAmelCase_    )

		if "encoder.pos_embed" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""encoder.pos_embed"""					,   """embeddings.position_embedding"""					,   lowerCAmelCase_    )
		if "encoder.cls_token" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""encoder.cls_token"""					,   """embeddings.class_embedding"""					,   lowerCAmelCase_    )

		if "self_attn" in key:
				SCREAMING_SNAKE_CASE__			=      re.sub(R"""self_attn.proj"""					,   """self_attn.projection"""					,   lowerCAmelCase_    )

		return key






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							str=None    ):


		if config_path is not None:
				SCREAMING_SNAKE_CASE__			=      BlipConfig.from_pretrained(lowerCAmelCase_    )
		else:
				SCREAMING_SNAKE_CASE__			=      BlipConfig(projection_dim=512					,   text_config={}					,   vision_config={}    )

		SCREAMING_SNAKE_CASE__			=      BlipForConditionalGeneration(lowerCAmelCase_    ).eval()

		SCREAMING_SNAKE_CASE__			=      "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

		SCREAMING_SNAKE_CASE__			=      blip_decoder(pretrained=lowerCAmelCase_					,   image_size=384					,   vit="""base"""    )
		SCREAMING_SNAKE_CASE__			=      pt_model.eval()

		SCREAMING_SNAKE_CASE__			=      pt_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE__			=      modified_state_dict.pop(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE__			=      rename_key(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE__			=      value

		hf_model.load_state_dict(lowerCAmelCase_    )

		SCREAMING_SNAKE_CASE__			=      384
		SCREAMING_SNAKE_CASE__			=      load_demo_image(image_size=lowerCAmelCase_					,   device="""cpu"""    )
		SCREAMING_SNAKE_CASE__			=      BertTokenizer.from_pretrained("""bert-base-uncased"""    )
		SCREAMING_SNAKE_CASE__			=      tokenizer(["""a picture of"""]    ).input_ids

		SCREAMING_SNAKE_CASE__			=      hf_model.generate(lowerCAmelCase_					,   lowerCAmelCase_    )

		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__			=      hf_model.generate(lowerCAmelCase_    )

		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(lowerCAmelCase_    )

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

		SCREAMING_SNAKE_CASE__			=      blip_vqa(pretrained=lowerCAmelCase_					,   image_size=lowerCAmelCase_					,   vit="""base"""    )
		vqa_model.eval()

		SCREAMING_SNAKE_CASE__			=      vqa_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE__			=      modified_state_dict.pop(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE__			=      rename_key(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE__			=      value

		SCREAMING_SNAKE_CASE__			=      BlipForQuestionAnswering(lowerCAmelCase_    )

		hf_vqa_model.load_state_dict(lowerCAmelCase_    )

		SCREAMING_SNAKE_CASE__			=      ["How many dogs are in this image?"]
		SCREAMING_SNAKE_CASE__			=      tokenizer(lowerCAmelCase_					,   return_tensors="""pt"""    ).input_ids

		SCREAMING_SNAKE_CASE__			=      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"""    )

		SCREAMING_SNAKE_CASE__			=      "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

		SCREAMING_SNAKE_CASE__			=      blip_itm(pretrained=lowerCAmelCase_					,   image_size=lowerCAmelCase_					,   vit="""base"""    )
		itm_model.eval()

		SCREAMING_SNAKE_CASE__			=      itm_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE__			=      modified_state_dict.pop(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE__			=      rename_key(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE__			=      value

		SCREAMING_SNAKE_CASE__			=      BlipForImageTextRetrieval(lowerCAmelCase_    )

		SCREAMING_SNAKE_CASE__			=      ["A picture of a woman with a dog sitting in a beach"]
		SCREAMING_SNAKE_CASE__			=      tokenizer(
		    lowerCAmelCase_					,   return_tensors="""pt"""					,   padding="""max_length"""					,   truncation=lowerCAmelCase_					,   max_length=35					,   ).input_ids

		hf_itm_model.load_state_dict(lowerCAmelCase_    )
		hf_itm_model.eval()

		SCREAMING_SNAKE_CASE__			=      hf_itm_model(lowerCAmelCase_					,   lowerCAmelCase_					,   use_itm_head=lowerCAmelCase_    )
		SCREAMING_SNAKE_CASE__			=      hf_itm_model(lowerCAmelCase_					,   lowerCAmelCase_					,   use_itm_head=lowerCAmelCase_    )

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0]					,   dim=1    )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
	_lowerCamelCase   				=			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')
	_lowerCamelCase   				=			parser.parse_args()

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













import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		def get_masked_lm_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_attention_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Any    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      array.reshape(UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		print(f'''Loading model based on config from {config_path}...'''    )
		SCREAMING_SNAKE_CASE__			=      BertConfig.from_json_file(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM(UpperCamelCase__    )

		# Layers
		for layer_index in range(0					,   config.num_hidden_layers    ):
				SCREAMING_SNAKE_CASE__			=      model.bert.encoder.layer[layer_index]

				# Self-attention
				SCREAMING_SNAKE_CASE__			=      layer.attention.self

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/kernel"""					,   self_attn.query.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/bias"""					,   self_attn.query.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/kernel"""					,   self_attn.key.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/bias"""					,   self_attn.key.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/kernel"""					,   self_attn.value.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/bias"""					,   self_attn.value.bias.data.shape    )

				# Self-attention Output
				SCREAMING_SNAKE_CASE__			=      layer.attention.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/kernel"""					,   self_output.dense.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/bias"""					,   self_output.dense.bias.data.shape    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/beta"""    )

				# Intermediate
				SCREAMING_SNAKE_CASE__			=      layer.intermediate

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/bias"""    )

				# Output
				SCREAMING_SNAKE_CASE__			=      layer.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/bias"""    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/beta"""    )

		# Embeddings
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_position_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_type_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/beta"""    )

		# LM Head
		SCREAMING_SNAKE_CASE__			=      model.cls.predictions.transform

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/bias"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/beta"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""embedding_table"""    )

		# Pooling
		SCREAMING_SNAKE_CASE__			=      BertPooler(config=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/bias"""    )

		# Export final model
		model.save_pretrained(UpperCamelCase__    )

		# Integration test - should load without any errors ;)
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM.from_pretrained(UpperCamelCase__    )
		print(new_model.eval()    )

		print("""Model conversion was done sucessfully!"""    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	parser.add_argument(
	    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
	)
	parser.add_argument(
	    '--bert_config_file',
	    type=str,
	    required=True,
	    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path',
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model.',
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)