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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor





class 					lowercase__						(   unittest.TestCase):


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Union[str, Any]=7    ,		UpperCamelCase__	:				Any=3    ,		UpperCamelCase__	:				Union[str, Any]=18    ,		UpperCamelCase__	:				Union[str, Any]=30    ,		UpperCamelCase__	:				Optional[Any]=400    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				int=None    ,		UpperCamelCase__	:				Tuple=True    ,		UpperCamelCase__	:				int=None    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				Tuple=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,		UpperCamelCase__	:				str=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,		UpperCamelCase__	:				Union[str, Any]=True    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							size if size is not None else {'''height''': 224, '''width''': 224}
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parent
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							batch_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							num_channels
												SCREAMING_SNAKE_CASE							:    int							=							image_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							min_resolution
												SCREAMING_SNAKE_CASE							:    int							=							max_resolution
												SCREAMING_SNAKE_CASE							:    Any							=							do_resize
												SCREAMING_SNAKE_CASE							:    List[str]							=							size
												SCREAMING_SNAKE_CASE							:    Any							=							do_center_crop
												SCREAMING_SNAKE_CASE							:    List[str]							=							crop_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							do_normalize
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image_mean
												SCREAMING_SNAKE_CASE							:    Dict							=							image_std
												SCREAMING_SNAKE_CASE							:    List[str]							=							do_convert_rgb


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_convert_rgb": self.do_convert_rgb,
												}



						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				Dict=False    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				Union[str, Any]=False						):





												'''simple docstring'''





												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		SCREAMING_SNAKE_CASE							:    Any							=							[]
																		for i in range(self.batch_size						):
																								image_inputs.append(
																								    np.random.randint(
																								        255    ,		size=(self.num_channels, self.max_resolution, self.max_resolution)    ,		dtype=np.uinta						)						)
												else:
																		SCREAMING_SNAKE_CASE							:    Dict							=							[]
																		for i in range(self.batch_size						):
																								SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							np.random.choice(np.arange(self.min_resolution    ,		self.max_resolution						)    ,		2						)
																								image_inputs.append(np.random.randint(255    ,		size=(self.num_channels, width, height)    ,		dtype=np.uinta						)						)

												if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							[Image.fromarray(np.moveaxis(UpperCamelCase__    ,		0    ,		-1						)						) for x in image_inputs]

												if torchify:
																		SCREAMING_SNAKE_CASE							:    Any							=							[torch.from_numpy(UpperCamelCase__						) for x in image_inputs]

												return image_inputs






@require_torch
@require_vision
class 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		ChineseCLIPImageProcessor if is_vision_available() else None


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							ChineseCLIPImageProcessingTester(self    ,		do_center_crop=UpperCamelCase__						)


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												return self.image_processor_tester.prepare_image_processor_dict()


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.image_processing_class(**self.image_processor_dict						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_resize'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''size'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_center_crop'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''center_crop'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_normalize'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''image_mean'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''image_std'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_convert_rgb'''						)						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processing_class.from_dict(self.image_processor_dict						)
												self.assertEqual(image_processor.size    ,		{'''height''': 224, '''width''': 224}						)
												self.assertEqual(image_processor.crop_size    ,		{'''height''': 18, '''width''': 18}						)

												SCREAMING_SNAKE_CASE							:    Any							=							self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42    ,		crop_size=84						)
												self.assertEqual(image_processor.size    ,		{'''shortest_edge''': 42}						)
												self.assertEqual(image_processor.crop_size    ,		{'''height''': 84, '''width''': 84}						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PIL images
												SCREAMING_SNAKE_CASE							:    str							=							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		Image.Image						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    List[str]							=							image_processing(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.image_processing_class(**self.image_processor_dict						)
												# create random numpy tensors
												SCREAMING_SNAKE_CASE							:    Any							=							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__    ,		numpify=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		np.ndarray						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    Dict							=							image_processing(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)



						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__    ,		torchify=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		torch.Tensor						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    int							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							image_processing(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)






@require_torch
@require_vision
class 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		ChineseCLIPImageProcessor if is_vision_available() else None


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							ChineseCLIPImageProcessingTester(self    ,		num_channels=4    ,		do_center_crop=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							3


						@property
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												return self.image_processor_tester.prepare_image_processor_dict()


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							self.image_processing_class(**self.image_processor_dict						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_resize'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''size'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_center_crop'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''center_crop'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_normalize'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''image_mean'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''image_std'''						)						)
												self.assertTrue(hasattr(UpperCamelCase__    ,		'''do_convert_rgb'''						)						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												pass



						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.image_processing_class(**self.image_processor_dict						)
												# create random PIL images
												SCREAMING_SNAKE_CASE							:    int							=							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__						)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__    ,		Image.Image						)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:    Dict							=							image_processing(image_inputs[0]    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )    ,		)

												# Test batched
												SCREAMING_SNAKE_CASE							:    List[str]							=							image_processing(UpperCamelCase__    ,		return_tensors='''pt'''						).pixel_values
												self.assertEqual(
												    encoded_images.shape    ,		(
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_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)





