



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

		a_     :      Optional[int] 						=     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)	),
		    ]	)
		a_     :      Tuple 						=     transform(SCREAMING_SNAKE_CASE__	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE__	)
		return image



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

		if "encoder.pos_embed" in key:
				a_     :      Any 						=     re.sub(r"encoder.pos_embed", "embeddings.position_embedding", SCREAMING_SNAKE_CASE__	)
		if "encoder.cls_token" in key:
				a_     :      Dict 						=     re.sub(r"encoder.cls_token", "embeddings.class_embedding", SCREAMING_SNAKE_CASE__	)

		if "self_attn" in key:
				a_     :      Optional[Any] 						=     re.sub(r"self_attn.proj", "self_attn.projection", SCREAMING_SNAKE_CASE__	)

		return key



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      Dict:
		if config_path is not None:
				a_     :      int 						=     BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Dict 						=     BlipConfig(projection_dim=512, text_config={}, vision_config={}	)

		a_     :      int 						=     BlipForConditionalGeneration(SCREAMING_SNAKE_CASE__	).eval()

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

		a_     :      Optional[Any] 						=     blip_decoder(pretrained=SCREAMING_SNAKE_CASE__, image_size=384, vit="base"	)
		a_     :      Optional[int] 						=     pt_model.eval()

		a_     :      Tuple 						=     pt_model.state_dict()
		for key in modified_state_dict.copy():
				a_     :      List[str] 						=     modified_state_dict.pop(SCREAMING_SNAKE_CASE__	)
				a_     :      Optional[Any] 						=     rename_key(SCREAMING_SNAKE_CASE__	)
				a_     :      Dict 						=     value

		hf_model.load_state_dict(SCREAMING_SNAKE_CASE__	)

		a_     :      int 						=     384
		a_     :      str 						=     load_demo_image(image_size=SCREAMING_SNAKE_CASE__, device="cpu"	)
		a_     :      Tuple 						=     BertTokenizer.from_pretrained("bert-base-uncased"	)
		a_     :      Dict 						=     tokenizer(["a picture of"]	).input_ids

		a_     :      Optional[Any] 						=     hf_model.generate(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		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]

		a_     :      Dict 						=     hf_model.generate(SCREAMING_SNAKE_CASE__	)

		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(SCREAMING_SNAKE_CASE__	)

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

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

		a_     :      str 						=     vqa_model.state_dict()
		for key in modified_state_dict.copy():
				a_     :      List[Any] 						=     modified_state_dict.pop(SCREAMING_SNAKE_CASE__	)
				a_     :      Union[str, Any] 						=     rename_key(SCREAMING_SNAKE_CASE__	)
				a_     :      Union[str, Any] 						=     value

		a_     :      str 						=     BlipForQuestionAnswering(SCREAMING_SNAKE_CASE__	)

		hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE__	)

		a_     :      Union[str, Any] 						=     ["How many dogs are in this image?"]
		a_     :      List[str] 						=     tokenizer(SCREAMING_SNAKE_CASE__, return_tensors="pt"	).input_ids

		a_     :      Dict 						=     hf_vqa_model.generate(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		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"	)

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

		a_     :      Optional[int] 						=     blip_itm(pretrained=SCREAMING_SNAKE_CASE__, image_size=SCREAMING_SNAKE_CASE__, vit="base"	)
		itm_model.eval()

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

		a_     :      Optional[int] 						=     BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE__	)

		a_     :      Union[str, Any] 						=     ["A picture of a woman with a dog sitting in a beach"]
		a_     :      Optional[int] 						=     tokenizer(
		    SCREAMING_SNAKE_CASE__, return_tensors="pt", padding="max_length", truncation=SCREAMING_SNAKE_CASE__, max_length=35, ).input_ids

		hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE__	)
		hf_itm_model.eval()

		a_     :      List[str] 						=     hf_itm_model(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, use_itm_head=SCREAMING_SNAKE_CASE__	)
		a_     :      Any 						=     hf_itm_model(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, use_itm_head=SCREAMING_SNAKE_CASE__	)

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

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




"""simple docstring"""
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      Any 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      int 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<unk>"      )
									self.assertEqual(vocab_keys[1]			,     "<s>"      )
									self.assertEqual(vocab_keys[-1]			,     "j"      )
									self.assertEqual(len(a_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

									a_     :      str 						=     tokenizer.tokenize("This is a test"      )
									self.assertListEqual(a_			,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

									a_     :      str 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]			,     )
									# fmt: on

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

									a_     :      Optional[int] 						=     tokenizer.convert_ids_to_tokens(a_      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
									# fmt: on






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(a_			,     a_      ):
											self.assertListEqual(tokenizer.encode_fast(a_      )			,     a_      )

									# Test that decode_fast returns the input text
									for text, token_ids in zip(a_			,     a_      ):
											self.assertEqual(tokenizer.decode_fast(a_      )			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
									    "Hey there, how are you doing this fine day?",
									    "This is a text with a trailing spaces followed by a dot     .",
									    "Häj sväjs lillebrör! =)",
									    "Det är inget fel på Mr. Cool",
									]

									# fmt: off
									a_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )