





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

     __A		:					str						=			transforms.Compose(
         [
             transforms.Resize((image_size, image_size)					,				interpolation=InterpolationMode.BICUBIC     ),
             transforms.ToTensor(),
             transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)					,				(0.26_862_954, 0.26_130_258, 0.27_577_711)     ),
         ]     )
     __A		:					List[str]						=			transform(lowercase_     ).unsqueeze(0     ).to(lowercase_     )
     return image






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:

     if "visual_encoder" in key:
          __A		:					List[Any]						=			re.sub('visual_encoder*'					,				'vision_model.encoder'					,				lowercase_     )
     if "blocks" in key:
          __A		:					int						=			re.sub(r'blocks'					,				'layers'					,				lowercase_     )
     if "attn" in key:
          __A		:					str						=			re.sub(r'attn'					,				'self_attn'					,				lowercase_     )
     if "norm1" in key:
          __A		:					Any						=			re.sub(r'norm1'					,				'layer_norm1'					,				lowercase_     )
     if "norm2" in key:
          __A		:					int						=			re.sub(r'norm2'					,				'layer_norm2'					,				lowercase_     )
     if "encoder.norm" in key:
          __A		:					List[str]						=			re.sub(r'encoder.norm'					,				'post_layernorm'					,				lowercase_     )
     if "encoder.patch_embed.proj" in key:
          __A		:					Optional[Any]						=			re.sub(r'encoder.patch_embed.proj'					,				'embeddings.patch_embedding'					,				lowercase_     )

     if "encoder.pos_embed" in key:
          __A		:					List[Any]						=			re.sub(r'encoder.pos_embed'					,				'embeddings.position_embedding'					,				lowercase_     )
     if "encoder.cls_token" in key:
          __A		:					List[Any]						=			re.sub(r'encoder.cls_token'					,				'embeddings.class_embedding'					,				lowercase_     )

     if "self_attn" in key:
          __A		:					Dict						=			re.sub(r'self_attn.proj'					,				'self_attn.projection'					,				lowercase_     )

     return key






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					Union[str, Any]=None     )	->	Optional[Any]:

     if config_path is not None:
          __A		:					str						=			BlipConfig.from_pretrained(lowercase_     )
     else:
          __A		:					Union[str, Any]						=			BlipConfig(projection_dim=5_12					,				text_config={}					,				vision_config={}     )

     __A		:					str						=			BlipForConditionalGeneration(lowercase_     ).eval()

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

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

     __A		:					int						=			pt_model.state_dict()
     for key in modified_state_dict.copy():
          __A		:					Any						=			modified_state_dict.pop(lowercase_     )
          __A		:					Optional[int]						=			rename_key(lowercase_     )
          __A		:					str						=			value

     hf_model.load_state_dict(lowercase_     )

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

     __A		:					Tuple						=			hf_model.generate(lowercase_					,				lowercase_     )

     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]

     __A		:					Dict						=			hf_model.generate(lowercase_     )

     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(lowercase_     )

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

     __A		:					Dict						=			blip_vqa(pretrained=lowercase_					,				image_size=lowercase_					,				vit='base'     )
     vqa_model.eval()

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

     __A		:					Tuple						=			BlipForQuestionAnswering(lowercase_     )

     hf_vqa_model.load_state_dict(lowercase_     )

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

     __A		:					Tuple						=			hf_vqa_model.generate(lowercase_					,				lowercase_     )
     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		:					str						=			blip_itm(pretrained=lowercase_					,				image_size=lowercase_					,				vit='base'     )
     itm_model.eval()

     __A		:					List[Any]						=			itm_model.state_dict()
     for key in modified_state_dict.copy():
          __A		:					Optional[Any]						=			modified_state_dict.pop(lowercase_     )
          __A		:					List[Any]						=			rename_key(lowercase_     )
          __A		:					Optional[Any]						=			value

     __A		:					Dict						=			BlipForImageTextRetrieval(lowercase_     )

     __A		:					List[Any]						=			["A picture of a woman with a dog sitting in a beach"]
     __A		:					List[str]						=			tokenizer(
         lowercase_					,				return_tensors='pt'					,				padding='max_length'					,				truncation=lowercase_					,				max_length=35					,				).input_ids

     hf_itm_model.load_state_dict(lowercase_     )
     hf_itm_model.eval()

     __A		:					Optional[Any]						=			hf_itm_model(lowercase_					,				lowercase_					,				use_itm_head=lowercase_     )
     __A		:					Union[str, Any]						=			hf_itm_model(lowercase_					,				lowercase_					,				use_itm_head=lowercase_     )

     assert out[0].item() == 0.2_110_687_494_277_954
     assert torch.nn.functional.softmax(out_itm[0]					,				dim=1     )[:, 1].item() == 0.45_698_845_386_505_127

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


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

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






"""simple docstring"""


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        lowercase__     (       lowercase_		,lowercase_=10      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						str    =							[]
      for _ in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
      return lrs




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[]
      for step in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        _UpperCamelCase   :						List[str]    =							os.path.join(lowercase_		,"schedule.bin"      )
                        torch.save(scheduler.state_dict()		,lowercase_      )

                        _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_      )
                        scheduler.load_state_dict(lowercase_      )
      return lrs



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Dict       , __a :							Any       , __a :							List[str]						)		->					List[str]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						Any    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							AdamW(params=[w]       , lr=2e-1       , weight_decay=0.0						)
        for _ in range(100						):
              _UpperCamelCase   :						List[Any]    =							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  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						Any    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						List[str]    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							Adafactor(
            params=[w]       , lr=1e-2       , eps=(1e-3_0, 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(1000						):
              _UpperCamelCase   :						Optional[Any]    =							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   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       nn.Linear(50		,	50 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       AdamW(m.parameters()		,	lr=10.0 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[str]						  =       10

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Tuple       , __a :							List[Any]       , __a :							Dict       , __a :							str=None						)		->					List[Any]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a       , msg=__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						Union[str, Any]    =							{"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        _UpperCamelCase   :						Optional[Any]    =							{
            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, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
            ),
            get_inverse_sqrt_schedule: (
                {"num_warmup_steps": 2},
                [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
            ),
        }

        for scheduler_func, data in scheds.items():
              _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							data

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              self.assertEqual(len([scheduler.get_lr()[0]]						)       , 1						)
              _UpperCamelCase   :						Optional[int]    =							unwrap_schedule(__a       , self.num_steps						)
              self.assertListAlmostEqual(
                  __a       , __a       , tol=1e-2       , msg=F'''failed for {scheduler_func} in normal scheduler'''       , )

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              if scheduler_func.__name__ != "get_constant_schedule":
                    LambdaScheduleWrapper.wrap_scheduler(__a						)  # wrap to test picklability of the schedule
              _UpperCamelCase   :						int    =							unwrap_and_save_reload_schedule(__a       , self.num_steps						)
              self.assertListEqual(__a       , __a       , msg=F'''failed for {scheduler_func} in save and reload'''						)





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Any    =							fn

  def __call__(		self :							Tuple       , *__a :							Optional[Any]       , **__a :							str						)		->					Any:
        return self.fn(*__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							list(map(self       , scheduler.lr_lambdas						)						)
