

'''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  (					A__  ,       A__   ):






 """simple docstring"""



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

 __lowercase													=				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													=				transform(A__   ).unsqueeze(0   ).to(A__   )
 return image


def 	_A  (					A__   ):






 """simple docstring"""



 if "visual_encoder" in key:
  __lowercase													=				re.sub('''visual_encoder*'''  ,       '''vision_model.encoder'''  ,       A__   )
 if "blocks" in key:
  __lowercase													=				re.sub(R'''blocks'''  ,       '''layers'''  ,       A__   )
 if "attn" in key:
  __lowercase													=				re.sub(R'''attn'''  ,       '''self_attn'''  ,       A__   )
 if "norm1" in key:
  __lowercase													=				re.sub(R'''norm1'''  ,       '''layer_norm1'''  ,       A__   )
 if "norm2" in key:
  __lowercase													=				re.sub(R'''norm2'''  ,       '''layer_norm2'''  ,       A__   )
 if "encoder.norm" in key:
  __lowercase													=				re.sub(R'''encoder.norm'''  ,       '''post_layernorm'''  ,       A__   )
 if "encoder.patch_embed.proj" in key:
  __lowercase													=				re.sub(R'''encoder.patch_embed.proj'''  ,       '''embeddings.patch_embedding'''  ,       A__   )

 if "encoder.pos_embed" in key:
  __lowercase													=				re.sub(R'''encoder.pos_embed'''  ,       '''embeddings.position_embedding'''  ,       A__   )
 if "encoder.cls_token" in key:
  __lowercase													=				re.sub(R'''encoder.cls_token'''  ,       '''embeddings.class_embedding'''  ,       A__   )

 if "self_attn" in key:
  __lowercase													=				re.sub(R'''self_attn.proj'''  ,       '''self_attn.projection'''  ,       A__   )

 return key


@torch.no_grad()
def 	_A  (					A__  ,       A__=None   ):






 """simple docstring"""



 if config_path is not None:
  __lowercase													=				BlipConfig.from_pretrained(A__   )
 else:
  __lowercase													=				BlipConfig(projection_dim=512  ,       text_config={}  ,       vision_config={}   )

 __lowercase													=				BlipForConditionalGeneration(A__   ).eval()

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

 __lowercase													=				blip_decoder(pretrained=A__  ,       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(A__   )
  __lowercase													=				rename_key(A__   )
  __lowercase													=				value

 hf_model.load_state_dict(A__   )

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

 __lowercase													=				hf_model.generate(A__  ,       A__   )

 assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

 __lowercase													=				hf_model.generate(A__   )

 assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

 if pytorch_dump_folder_path is not None:
  hf_model.save_pretrained(A__   )

 # 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=A__  ,       image_size=A__  ,       vit='''base'''   )
 vqa_model.eval()

 __lowercase													=				vqa_model.state_dict()
 for key in modified_state_dict.copy():
  __lowercase													=				modified_state_dict.pop(A__   )
  __lowercase													=				rename_key(A__   )
  __lowercase													=				value

 __lowercase													=				BlipForQuestionAnswering(A__   )

 hf_vqa_model.load_state_dict(A__   )

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

 __lowercase													=				hf_vqa_model.generate(A__  ,       A__   )
 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=A__  ,       image_size=A__  ,       vit='''base'''   )
 itm_model.eval()

 __lowercase													=				itm_model.state_dict()
 for key in modified_state_dict.copy():
  __lowercase													=				modified_state_dict.pop(A__   )
  __lowercase													=				rename_key(A__   )
  __lowercase													=				value

 __lowercase													=				BlipForImageTextRetrieval(A__   )

 __lowercase													=				['''A picture of a woman with a dog sitting in a beach''']
 __lowercase													=				tokenizer(
     A__  ,       return_tensors='''pt'''  ,       padding='''max_length'''  ,       truncation=A__  ,       max_length=35  ,       ).input_ids

 hf_itm_model.load_state_dict(A__   )
 hf_itm_model.eval()

 __lowercase													=				hf_itm_model(A__  ,       A__  ,       use_itm_head=A__   )
 __lowercase													=				hf_itm_model(A__  ,       A__  ,       use_itm_head=A__   )

 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'''






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				hashlib.mda(image.tobytes()   )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]    ):
   __lowercase													=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}      ,lowercase__    )
   import datasets

   __lowercase													=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''    )
   __lowercase													=				depth_estimator(
       [
           Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           # RGBA
           dataset[0]['''file'''],
           # LA
           dataset[1]['''file'''],
           # L
           dataset[2]['''file'''],
       ]    )
   self.assertEqual(
       [
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @slow
  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''Intel/dpt-large'''
   __lowercase													=				pipeline('''depth-estimation'''      ,model=lowercase__    )
   __lowercase													=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
   __lowercase													=				hashimage(outputs['''depth''']    )

   # This seems flaky.
   # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )      ,2_9.3_0_4    )
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )      ,2.6_6_2    )






  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   # This is highly irregular to have no small tests.
   self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

