






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    Adafactor,
		    AdamW,
		    get_constant_schedule,
		    get_constant_schedule_with_warmup,
		    get_cosine_schedule_with_warmup,
		    get_cosine_with_hard_restarts_schedule_with_warmup,
		    get_inverse_sqrt_schedule,
		    get_linear_schedule_with_warmup,
		    get_polynomial_decay_schedule_with_warmup,
		)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=10       ):






				'''simple docstring'''
				lowercase__     				=    []
				for _ in range(UpperCAmelCase__       ):
								lrs.append(scheduler.get_lr()[0]       )
								scheduler.step()
				return lrs






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=10       ):






				'''simple docstring'''
				lowercase__     				=    []
				for step in range(UpperCAmelCase__       ):
								lrs.append(scheduler.get_lr()[0]       )
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase__     				=    os.path.join(UpperCAmelCase__	,     '''schedule.bin'''       )
																torch.save(scheduler.state_dict()	,     UpperCAmelCase__       )

																lowercase__     				=    torch.load(UpperCAmelCase__       )
																scheduler.load_state_dict(UpperCAmelCase__       )
				return lrs
@require_torch
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					List[str],						lowerCamelCase		:					int						):

								'''simple docstring'''
								self.assertEqual(len(_A						),						len(_A						)						)
								for a, b in zip(_A,						_A						):
												self.assertAlmostEqual(_A,						_A,						delta=_A						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    torch.tensor([0.1, -0.2, -0.1],						requires_grad=_A						)
								lowercase__     				=    torch.tensor([0.4, 0.2, -0.5]						)
								lowercase__     				=    nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								lowercase__     				=    AdamW(params=[w],						lr=2E-1,						weight_decay=0.0						)
								for _ in range(100						):
												lowercase__     				=    criterion(_A,						_A						)
												loss.backward()
												optimizer.step()
												w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
												w.grad.zero_()
								self.assertListAlmostEqual(w.tolist(),						[0.4, 0.2, -0.5],						tol=1E-2						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    torch.tensor([0.1, -0.2, -0.1],						requires_grad=_A						)
								lowercase__     				=    torch.tensor([0.4, 0.2, -0.5]						)
								lowercase__     				=    nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								lowercase__     				=    Adafactor(
								    params=[w],						lr=1E-2,						eps=(1E-30, 1E-3),						clip_threshold=1.0,						decay_rate=-0.8,						betaa=_A,						weight_decay=0.0,						relative_step=_A,						scale_parameter=_A,						warmup_init=_A,						)
								for _ in range(1_000						):
												lowercase__     				=    criterion(_A,						_A						)
												loss.backward()
												optimizer.step()
												w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
												w.grad.zero_()
								self.assertListAlmostEqual(w.tolist(),						[0.4, 0.2, -0.5],						tol=1E-2						)



@require_torch
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= nn.Linear(50	,50			) if is_torch_available() else None
				lowercase__       							= AdamW(m.parameters()	,lr=10.0			) if is_torch_available() else None
				lowercase__       							= 10




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					str,						lowerCamelCase		:					Dict=None						):

								'''simple docstring'''
								self.assertEqual(len(_A						),						len(_A						)						)
								for a, b in zip(_A,						_A						):
												self.assertAlmostEqual(_A,						_A,						delta=_A,						msg=_A						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								lowercase__     				=    {
								    get_constant_schedule: ({}, [10.0] * self.num_steps),
								    get_constant_schedule_with_warmup: (
								        {'''num_warmup_steps''': 4},
								        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
								    ),
								    get_linear_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
								    ),
								    get_cosine_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
								    ),
								    get_cosine_with_hard_restarts_schedule_with_warmup: (
								        {**common_kwargs, '''num_cycles''': 2},
								        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
								    ),
								    get_polynomial_decay_schedule_with_warmup: (
								        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
								        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
								    ),
								    get_inverse_sqrt_schedule: (
								        {'''num_warmup_steps''': 2},
								        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
								    ),
								}

								for scheduler_func, data in scheds.items():
												lowercase__				,		lowercase__     				=    data

												lowercase__     				=    scheduler_func(self.optimizer,						**_A						)
												self.assertEqual(len([scheduler.get_lr()[0]]						),						1						)
												lowercase__     				=    unwrap_schedule(_A,						self.num_steps						)
												self.assertListAlmostEqual(
												    _A,						_A,						tol=1E-2,						msg=F"""failed for {scheduler_func} in normal scheduler""",						)

												lowercase__     				=    scheduler_func(self.optimizer,						**_A						)
												if scheduler_func.__name__ != "get_constant_schedule":
																LambdaScheduleWrapper.wrap_scheduler(_A						)  # wrap to test picklability of the schedule
												lowercase__     				=    unwrap_and_save_reload_schedule(_A,						self.num_steps						)
												self.assertListEqual(_A,						_A,						msg=F"""failed for {scheduler_func} in save and reload"""						)



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    fn




				def __call__(  self		:					int,						*lowerCamelCase		:					Union[str, Any],						**lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								return self.fn(*_A,						**_A						)




				@classmethod
				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    list(map(self,						scheduler.lr_lambdas						)						)












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    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
				lowercase__     				=    Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_       ).raw       ).convert('''RGB'''       )

				lowercase__     				=    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)       ),
				    ]       )
				lowercase__     				=    transform(lowerCamelCase_       ).unsqueeze(0       ).to(lowerCamelCase_       )
				return image






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if "visual_encoder" in key:
								lowercase__     				=    re.sub('''visual_encoder*'''	,     '''vision_model.encoder'''	,     lowerCamelCase_       )
				if "blocks" in key:
								lowercase__     				=    re.sub(r'''blocks'''	,     '''layers'''	,     lowerCamelCase_       )
				if "attn" in key:
								lowercase__     				=    re.sub(r'''attn'''	,     '''self_attn'''	,     lowerCamelCase_       )
				if "norm1" in key:
								lowercase__     				=    re.sub(r'''norm1'''	,     '''layer_norm1'''	,     lowerCamelCase_       )
				if "norm2" in key:
								lowercase__     				=    re.sub(r'''norm2'''	,     '''layer_norm2'''	,     lowerCamelCase_       )
				if "encoder.norm" in key:
								lowercase__     				=    re.sub(r'''encoder.norm'''	,     '''post_layernorm'''	,     lowerCamelCase_       )
				if "encoder.patch_embed.proj" in key:
								lowercase__     				=    re.sub(r'''encoder.patch_embed.proj'''	,     '''embeddings.patch_embedding'''	,     lowerCamelCase_       )

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

				if "self_attn" in key:
								lowercase__     				=    re.sub(r'''self_attn.proj'''	,     '''self_attn.projection'''	,     lowerCamelCase_       )

				return key






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				if config_path is not None:
								lowercase__     				=    BlipConfig.from_pretrained(lowerCamelCase_       )
				else:
								lowercase__     				=    BlipConfig(projection_dim=512	,     text_config={}	,     vision_config={}       )

				lowercase__     				=    BlipForConditionalGeneration(lowerCamelCase_       ).eval()

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

				lowercase__     				=    blip_decoder(pretrained=lowerCamelCase_	,     image_size=384	,     vit='''base'''       )
				lowercase__     				=    pt_model.eval()

				lowercase__     				=    pt_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				hf_model.load_state_dict(lowerCamelCase_       )

				lowercase__     				=    384
				lowercase__     				=    load_demo_image(image_size=lowerCamelCase_	,     device='''cpu'''       )
				lowercase__     				=    BertTokenizer.from_pretrained('''bert-base-uncased'''       )
				lowercase__     				=    tokenizer(['''a picture of''']       ).input_ids

				lowercase__     				=    hf_model.generate(lowerCamelCase_	,     lowerCamelCase_       )

				assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

				lowercase__     				=    hf_model.generate(lowerCamelCase_       )

				assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 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'
				lowercase__     				=    (
				    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
				)

				lowercase__     				=    blip_vqa(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				vqa_model.eval()

				lowercase__     				=    vqa_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForQuestionAnswering(lowerCamelCase_       )

				hf_vqa_model.load_state_dict(lowerCamelCase_       )

				lowercase__     				=    ['''How many dogs are in this image?''']
				lowercase__     				=    tokenizer(lowerCamelCase_	,     return_tensors='''pt'''       ).input_ids

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

				lowercase__     				=    blip_itm(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				itm_model.eval()

				lowercase__     				=    itm_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForImageTextRetrieval(lowerCamelCase_       )

				lowercase__     				=    ['''A picture of a woman with a dog sitting in a beach''']
				lowercase__     				=    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__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )
				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )

				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__":
							A__			:    Optional[int]         =				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__			:    List[Any]         =				parser.parse_args()

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




