



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

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



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

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

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

		return key



@torch.no_grad()
def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=None	)							->      Optional[int]:
		if config_path is not None:
				lowercase__   :    Union[str, Any]				=			BlipConfig.from_pretrained(__lowerCamelCase	)
		else:
				lowercase__   :    str				=			BlipConfig(projection_dim=5_12						,     text_config={}						,     vision_config={}	)

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

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

		lowercase__   :    Optional[int]				=			blip_decoder(pretrained=__lowerCamelCase						,     image_size=3_84						,     vit='''base'''	)
		lowercase__   :    Optional[int]				=			pt_model.eval()

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

		hf_model.load_state_dict(__lowerCamelCase	)

		lowercase__   :    int				=			3_84
		lowercase__   :    Optional[int]				=			load_demo_image(image_size=__lowerCamelCase						,     device='''cpu'''	)
		lowercase__   :    str				=			BertTokenizer.from_pretrained('''bert-base-uncased'''	)
		lowercase__   :    Dict				=			tokenizer(['''a picture of''']	).input_ids

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

		assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

		lowercase__   :    Tuple				=			hf_model.generate(__lowerCamelCase	)

		assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

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

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

		lowercase__   :    Tuple				=			BlipForQuestionAnswering(__lowerCamelCase	)

		hf_vqa_model.load_state_dict(__lowerCamelCase	)

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

		lowercase__   :    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'''	)

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

		lowercase__   :    str				=			blip_itm(pretrained=__lowerCamelCase						,     image_size=__lowerCamelCase						,     vit='''base'''	)
		itm_model.eval()

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

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

		lowercase__   :    Any				=			['''A picture of a woman with a dog sitting in a beach''']
		lowercase__   :    Optional[Any]				=			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()

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





"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
							from transformers import (
							    AutoConfig,
							    BertConfig,
							    GPTaConfig,
							    TaConfig,
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
							from transformers import (
							    AutoModel,
							    AutoModelForCausalLM,
							    AutoModelForMaskedLM,
							    AutoModelForPreTraining,
							    AutoModelForQuestionAnswering,
							    AutoModelForSeqaSeqLM,
							    AutoModelForSequenceClassification,
							    AutoModelWithLMHead,
							    BertForMaskedLM,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    BertModel,
							    GPTaLMHeadModel,
							    RobertaForMaskedLM,
							    TaForConditionalGeneration,
							)
@is_pt_tf_cross_test
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@slow
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Dict				=			TFAutoModel.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModel.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Tuple:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Dict				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForPreTraining.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			AutoModelForPreTraining.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForCausalLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			TFAutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForCausalLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			AutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Any						)					->		Tuple:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    str				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			TFAutoModelForMaskedLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    str				=			TFAutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModelForMaskedLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Any				=			AutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Union[str, Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[int]				=			AutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			TFAutoModelForSequenceClassification.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForSequenceClassification.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    List[Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    Union[str, Any]				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)







		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    int				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)
