




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor





class     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: List[Any]=13    ,       UpperCamelCase_: Any=3    ,       UpperCamelCase_: Union[str, Any]=2_24    ,       UpperCamelCase_: List[Any]=30    ,       UpperCamelCase_: int=4_00    ,       UpperCamelCase_: Optional[Any]=True    ,       UpperCamelCase_: str=None    ,       UpperCamelCase_: Union[str, Any]=True    ,       UpperCamelCase_: Any=[0.5, 0.5, 0.5]    ,       UpperCamelCase_: Any=[0.5, 0.5, 0.5]    ,       ):
             __lowerCamelCase   =						size if size is not None else {'''height''': 18, '''width''': 18}
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						num_channels
             __lowerCamelCase   =						image_size
             __lowerCamelCase   =						min_resolution
             __lowerCamelCase   =						max_resolution
             __lowerCamelCase   =						do_resize
             __lowerCamelCase   =						size
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						image_mean
             __lowerCamelCase   =						image_std




      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
             }





@require_torch
@require_vision
class     lowerCamelCase__(					UpperCamelCase_ ,     unittest.TestCase):
      UpperCAmelCase__      :	Optional[Any]    		=   ViTImageProcessor if is_vision_available() else None
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						EfficientFormerImageProcessorTester(self   )
      @property
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             return self.image_proc_tester.prepare_image_processor_dict()
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(UpperCamelCase__    ,       """image_mean"""   )   )
             self.assertTrue(hasattr(UpperCamelCase__    ,       """image_std"""   )   )
             self.assertTrue(hasattr(UpperCamelCase__    ,       """do_normalize"""   )   )
             self.assertTrue(hasattr(UpperCamelCase__    ,       """do_resize"""   )   )
             self.assertTrue(hasattr(UpperCamelCase__    ,       """size"""   )   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             pass
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             __lowerCamelCase   =						prepare_image_inputs(self.image_proc_tester    ,       equal_resolution=UpperCamelCase__   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase__    ,       Image.Image   )

             # Test not batched input
             __lowerCamelCase   =						image_processor(image_inputs[0]    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     1,
                     self.image_proc_tester.num_channels,
                     self.image_proc_tester.size["""height"""],
                     self.image_proc_tester.size["""width"""],
                 )    ,       )

             # Test batched
             __lowerCamelCase   =						image_processor(UpperCamelCase__    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     self.image_proc_tester.batch_size,
                     self.image_proc_tester.num_channels,
                     self.image_proc_tester.size["""height"""],
                     self.image_proc_tester.size["""width"""],
                 )    ,       )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             __lowerCamelCase   =						prepare_image_inputs(self.image_proc_tester    ,       equal_resolution=UpperCamelCase__    ,       numpify=UpperCamelCase__   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase__    ,       np.ndarray   )

             # Test not batched input
             __lowerCamelCase   =						image_processor(image_inputs[0]    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     1,
                     self.image_proc_tester.num_channels,
                     self.image_proc_tester.size["""height"""],
                     self.image_proc_tester.size["""width"""],
                 )    ,       )

             # Test batched
             __lowerCamelCase   =						image_processor(UpperCamelCase__    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     self.image_proc_tester.batch_size,
                     self.image_proc_tester.num_channels,
                     self.image_proc_tester.size["""height"""],
                     self.image_proc_tester.size["""width"""],
                 )    ,       )




      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             __lowerCamelCase   =						prepare_image_inputs(self.image_proc_tester    ,       equal_resolution=UpperCamelCase__    ,       torchify=UpperCamelCase__   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase__    ,       torch.Tensor   )

             # Test not batched input
             __lowerCamelCase   =						image_processor(image_inputs[0]    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     1,
                     self.image_proc_tester.num_channels,
                     self.image_proc_tester.size["""height"""],
                     self.image_proc_tester.size["""width"""],
                 )    ,       )

             # Test batched
             __lowerCamelCase   =						image_processor(UpperCamelCase__    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     self.image_proc_tester.batch_size,
                     self.image_proc_tester.num_channels,
                     self.image_proc_tester.size["""height"""],
                     self.image_proc_tester.size["""width"""],
                 )    ,       )


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	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Image.open(requests.get(_lowercase		,  stream=_lowercase						).raw						).convert('''RGB'''						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)		,  interpolation=InterpolationMode.BICUBIC						),
						        transforms.ToTensor(),
						        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)		,  (0.2686_2954, 0.2613_0258, 0.2757_7711)						),
						    ]						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							transform(_lowercase						).unsqueeze(0						).to(_lowercase						)
						return image
def       A	(    _lowercase						):
						if "visual_encoder" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub('''visual_encoder*'''		,  '''vision_model.encoder'''		,  _lowercase						)
						if "blocks" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''blocks'''		,  '''layers'''		,  _lowercase						)
						if "attn" in key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							re.sub(R'''attn'''		,  '''self_attn'''		,  _lowercase						)
						if "norm1" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''norm1'''		,  '''layer_norm1'''		,  _lowercase						)
						if "norm2" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''norm2'''		,  '''layer_norm2'''		,  _lowercase						)
						if "encoder.norm" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub(R'''encoder.norm'''		,  '''post_layernorm'''		,  _lowercase						)
						if "encoder.patch_embed.proj" in key:
												SCREAMING_SNAKE_CASE							:    Dict							=							re.sub(R'''encoder.patch_embed.proj'''		,  '''embeddings.patch_embedding'''		,  _lowercase						)

						if "encoder.pos_embed" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''encoder.pos_embed'''		,  '''embeddings.position_embedding'''		,  _lowercase						)
						if "encoder.cls_token" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''encoder.cls_token'''		,  '''embeddings.class_embedding'''		,  _lowercase						)

						if "self_attn" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''self_attn.proj'''		,  '''self_attn.projection'''		,  _lowercase						)

						return key
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase=None						):
						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Any							=							BlipConfig.from_pretrained(_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							BlipConfig(projection_dim=512		,  text_config={}		,  vision_config={}						)

						SCREAMING_SNAKE_CASE							:    str							=							BlipForConditionalGeneration(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_decoder(pretrained=_lowercase		,  image_size=384		,  vit='''base'''						)
						SCREAMING_SNAKE_CASE							:    Any							=							pt_model.eval()

						SCREAMING_SNAKE_CASE							:    Dict							=							pt_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Tuple							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Any							=							value

						hf_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							384
						SCREAMING_SNAKE_CASE							:    Tuple							=							load_demo_image(image_size=_lowercase		,  device='''cpu'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							BertTokenizer.from_pretrained('''bert-base-uncased'''						)
						SCREAMING_SNAKE_CASE							:    int							=							tokenizer(['''a picture of''']						).input_ids

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							hf_model.generate(_lowercase		,  _lowercase						)

						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]

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_model.generate(_lowercase						)

						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(_lowercase						)

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

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_vqa(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						vqa_model.eval()

						SCREAMING_SNAKE_CASE							:    List[str]							=							vqa_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							value

						SCREAMING_SNAKE_CASE							:    Dict							=							BlipForQuestionAnswering(_lowercase						)

						hf_vqa_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Dict							=							['''How many dogs are in this image?''']
						SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(_lowercase		,  return_tensors='''pt'''						).input_ids

						SCREAMING_SNAKE_CASE							:    Dict							=							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'''						)

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

						SCREAMING_SNAKE_CASE							:    int							=							blip_itm(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						itm_model.eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							itm_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    int							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Dict							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							value

						SCREAMING_SNAKE_CASE							:    int							=							BlipForImageTextRetrieval(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							['''A picture of a woman with a dog sitting in a beach''']
						SCREAMING_SNAKE_CASE							:    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()

						SCREAMING_SNAKE_CASE							:    int							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)

						assert out[0].item() == 0.2110_6874_9427_7954
						assert torch.nn.functional.softmax(out_itm[0]		,  dim=1						)[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
					__UpperCamelCase		:       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')
					__UpperCamelCase		:       int									=     parser.parse_args()

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





