






"""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    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Optional[Any]  )    ->							Any:
						__magic_name__:	int							=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
						__magic_name__:	Optional[int]							=       Image.open(requests.get(__UpperCAmelCase      ,   stream=__UpperCAmelCase  ).raw  ).convert("""RGB"""  )

						__magic_name__:	Union[str, Any]							=       transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)      ,   interpolation=InterpolationMode.BICUBIC  ),
						        transforms.ToTensor(),
						        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)      ,   (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)  ),
						    ]  )
						__magic_name__:	Any							=       transform(__UpperCAmelCase  ).unsqueeze(0  ).to(__UpperCAmelCase  )
						return image



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

						if "encoder.pos_embed" in key:
												__magic_name__:	List[Any]							=       re.sub(R"""encoder.pos_embed"""      ,   """embeddings.position_embedding"""      ,   __UpperCAmelCase  )
						if "encoder.cls_token" in key:
												__magic_name__:	Dict							=       re.sub(R"""encoder.cls_token"""      ,   """embeddings.class_embedding"""      ,   __UpperCAmelCase  )

						if "self_attn" in key:
												__magic_name__:	Optional[Any]							=       re.sub(R"""self_attn.proj"""      ,   """self_attn.projection"""      ,   __UpperCAmelCase  )

						return key



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Optional[int]=None  )    ->							Tuple:
						if config_path is not None:
												__magic_name__:	Optional[Any]							=       BlipConfig.from_pretrained(__UpperCAmelCase  )
						else:
												__magic_name__:	str							=       BlipConfig(projection_dim=5_1_2      ,   text_config={}      ,   vision_config={}  )

						__magic_name__:	Union[str, Any]							=       BlipForConditionalGeneration(__UpperCAmelCase  ).eval()

						__magic_name__:	List[str]							=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

						__magic_name__:	Optional[Any]							=       blip_decoder(pretrained=__UpperCAmelCase      ,   image_size=3_8_4      ,   vit="""base"""  )
						__magic_name__:	Optional[Any]							=       pt_model.eval()

						__magic_name__:	int							=       pt_model.state_dict()
						for key in modified_state_dict.copy():
												__magic_name__:	Tuple							=       modified_state_dict.pop(__UpperCAmelCase  )
												__magic_name__:	str							=       rename_key(__UpperCAmelCase  )
												__magic_name__:	Dict							=       value

						hf_model.load_state_dict(__UpperCAmelCase  )

						__magic_name__:	Optional[int]							=       3_8_4
						__magic_name__:	List[Any]							=       load_demo_image(image_size=__UpperCAmelCase      ,   device="""cpu"""  )
						__magic_name__:	int							=       BertTokenizer.from_pretrained("""bert-base-uncased"""  )
						__magic_name__:	Any							=       tokenizer(["""a picture of"""]  ).input_ids

						__magic_name__:	List[str]							=       hf_model.generate(__UpperCAmelCase      ,   __UpperCAmelCase  )

						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]

						__magic_name__:	List[str]							=       hf_model.generate(__UpperCAmelCase  )

						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(__UpperCAmelCase  )

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

						__magic_name__:	Optional[int]							=       blip_vqa(pretrained=__UpperCAmelCase      ,   image_size=__UpperCAmelCase      ,   vit="""base"""  )
						vqa_model.eval()

						__magic_name__:	Optional[Any]							=       vqa_model.state_dict()
						for key in modified_state_dict.copy():
												__magic_name__:	Dict							=       modified_state_dict.pop(__UpperCAmelCase  )
												__magic_name__:	List[str]							=       rename_key(__UpperCAmelCase  )
												__magic_name__:	Optional[int]							=       value

						__magic_name__:	Tuple							=       BlipForQuestionAnswering(__UpperCAmelCase  )

						hf_vqa_model.load_state_dict(__UpperCAmelCase  )

						__magic_name__:	Any							=       ["""How many dogs are in this image?"""]
						__magic_name__:	Tuple							=       tokenizer(__UpperCAmelCase      ,   return_tensors="""pt"""  ).input_ids

						__magic_name__:	Optional[int]							=       hf_vqa_model.generate(__UpperCAmelCase      ,   __UpperCAmelCase  )
						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"""  )

						__magic_name__:	Optional[Any]							=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

						__magic_name__:	Any							=       blip_itm(pretrained=__UpperCAmelCase      ,   image_size=__UpperCAmelCase      ,   vit="""base"""  )
						itm_model.eval()

						__magic_name__:	Union[str, Any]							=       itm_model.state_dict()
						for key in modified_state_dict.copy():
												__magic_name__:	int							=       modified_state_dict.pop(__UpperCAmelCase  )
												__magic_name__:	Tuple							=       rename_key(__UpperCAmelCase  )
												__magic_name__:	str							=       value

						__magic_name__:	Optional[Any]							=       BlipForImageTextRetrieval(__UpperCAmelCase  )

						__magic_name__:	Dict							=       ["""A picture of a woman with a dog sitting in a beach"""]
						__magic_name__:	int							=       tokenizer(
						    __UpperCAmelCase      ,   return_tensors="""pt"""      ,   padding="""max_length"""      ,   truncation=__UpperCAmelCase      ,   max_length=3_5      ,   ).input_ids

						hf_itm_model.load_state_dict(__UpperCAmelCase  )
						hf_itm_model.eval()

						__magic_name__:	Optional[int]							=       hf_itm_model(__UpperCAmelCase      ,   __UpperCAmelCase      ,   use_itm_head=__UpperCAmelCase  )
						__magic_name__:	Optional[Any]							=       hf_itm_model(__UpperCAmelCase      ,   __UpperCAmelCase      ,   use_itm_head=__UpperCAmelCase  )

						assert out[0].item() == 0.21_10_68_74_94_27_79_54
						assert torch.nn.functional.softmax(out_itm[0]      ,   dim=1  )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

						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)










"""simple docstring"""






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[Any]=None			)    ->			str:



			'''simple docstring'''
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
			a__  :	Optional[Any]		 =			nn.Parameter(lowerCAmelCase__			)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
						a__  :	str		 =			nn.Parameter(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	Tuple		 =			np.asarray(weights[0]			)
			a__  :	Any		 =			np.asarray(weights[1]			)
			a__  :	Optional[Any]		 =			np.asarray(weights[2]			)

			set_param(
			    torch_layer.self_attention.query_key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	int		 =			np.asarray(weights[0]			)
			a__  :	List[str]		 =			np.asarray(weights[1]			)
			a__  :	Optional[int]		 =			np.asarray(weights[2]			)
			a__  :	int		 =			np.asarray(weights[3]			)

			set_param(
			    torch_layer.self_attention.query							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# layernorm 1
			a__  :	int		 =			weights[0][0][0]
			a__  :	Optional[Any]		 =			np.asarray(layer_norm_a[0]			)
			a__  :	Optional[int]		 =			np.asarray(layer_norm_a[1]			)
			set_param(
			    torch_block.attention.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# lsh weights + output
			a__  :	Dict		 =			weights[0][1]
			if len(lowerCAmelCase__			) < 4:
						set_layer_weights_in_torch_lsh(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)
			else:
						set_layer_weights_in_torch_local(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)

			# intermediate weighs
			a__  :	Optional[int]		 =			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(lowerCAmelCase__			) == 4:
						a__  :	List[str]		 =			intermediate_weights[2]

			# layernorm 2
			a__  :	List[Any]		 =			np.asarray(intermediate_weights[0][0]			)
			a__  :	Optional[int]		 =			np.asarray(intermediate_weights[0][1]			)
			set_param(
			    torch_block.feed_forward.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate dense
			a__  :	Tuple		 =			np.asarray(intermediate_weights[1][0]			)
			a__  :	Union[str, Any]		 =			np.asarray(intermediate_weights[1][1]			)
			set_param(
			    torch_block.feed_forward.dense.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate out
			a__  :	Optional[Any]		 =			np.asarray(intermediate_weights[4][0]			)
			a__  :	Dict		 =			np.asarray(intermediate_weights[4][1]			)
			set_param(
			    torch_block.feed_forward.output.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# reformer model
			a__  :	int		 =			torch_model.reformer

			# word embeds
			a__  :	List[Any]		 =			np.asarray(weights[1]			)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(lowerCAmelCase__			)							,			)

			if isinstance(weights[3]							,			lowerCAmelCase__			):
						a__  :	Dict		 =			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights			)			):
									a__  :	str		 =			np.asarray(weights[3][emb_idx][0]			)
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), F"{position_embeddings[emb_idx]} emb does not match"
									a__  :	List[str]		 =			nn.Parameter(torch.tensor(lowerCAmelCase__			)			)

			a__  :	Optional[Any]		 =			weights[5]
			assert len(torch_model_reformer.encoder.layers			) * 4 == len(
			    lowerCAmelCase__			), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
						a__  :	Union[str, Any]		 =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# output layer norm
			a__  :	str		 =			np.asarray(weights[7][0]			)
			a__  :	List[Any]		 =			np.asarray(weights[7][1]			)
			set_param(
			    torch_model_reformer.encoder.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# output embeddings
			a__  :	str		 =			np.asarray(weights[9][0]			)
			a__  :	Optional[int]		 =			np.asarray(weights[9][1]			)
			set_param(
			    torch_model.lm_head.decoder							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	List[Any]		 =			ReformerConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	Optional[Any]		 =			ReformerModelWithLMHead(lowerCAmelCase__			)

			with open(lowerCAmelCase__							,			"rb"			) as f:
						a__  :	List[str]		 =			pickle.load(lowerCAmelCase__			)["weights"]

			set_model_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			config.hidden_size			)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"			)
			torch.save(model.state_dict()							,			lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained Reformer model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)