import os
import sys
import unittest


__UpperCamelCase		:       List[str]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCamelCase		:       str									=     os.path.join(git_repo_path, 'src', 'transformers')

__UpperCamelCase		:       List[str]									=     '\n{0} = None\n'

__UpperCamelCase		:       Any									=     '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


__UpperCamelCase		:       Union[str, Any]									=     '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'





class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''						)
												self.assertIsNone(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							find_backend('''    if not is_tokenizers_available():'''						)
												self.assertEqual(UpperCamelCase__    ,		'''tokenizers'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							find_backend('''    if not is_tensorflow_text_available():'''						)
												self.assertEqual(UpperCamelCase__    ,		'''tensorflow_text'''						)

												SCREAMING_SNAKE_CASE							:    Any							=							find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''						)
												self.assertEqual(UpperCamelCase__    ,		'''sentencepiece_and_tokenizers'''						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							find_backend(
												    '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''						)
												self.assertEqual(UpperCamelCase__    ,		'''sentencepiece_and_tensorflow_text'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							find_backend(
												    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''						)
												self.assertEqual(UpperCamelCase__    ,		'''sentencepiece_and_tokenizers_and_vision'''						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							read_init()
												# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
												self.assertIn('''torch'''    ,		UpperCamelCase__						)
												self.assertIn('''tensorflow_text'''    ,		UpperCamelCase__						)
												self.assertIn('''sentencepiece_and_tokenizers'''    ,		UpperCamelCase__						)

												# Likewise, we can't assert on the exact content of a key
												self.assertIn('''BertModel'''    ,		objects['''torch''']						)
												self.assertIn('''TFBertModel'''    ,		objects['''tf''']						)
												self.assertIn('''FlaxBertModel'''    ,		objects['''flax''']						)
												self.assertIn('''BertModel'''    ,		objects['''torch''']						)
												self.assertIn('''TFBertTokenizer'''    ,		objects['''tensorflow_text''']						)
												self.assertIn('''convert_slow_tokenizer'''    ,		objects['''sentencepiece_and_tokenizers''']						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							create_dummy_object('''CONSTANT'''    ,		'''\'torch\''''						)
												self.assertEqual(UpperCamelCase__    ,		'''\nCONSTANT = None\n'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							create_dummy_object('''function'''    ,		'''\'torch\''''						)
												self.assertEqual(
												    UpperCamelCase__    ,		'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''						)

												SCREAMING_SNAKE_CASE							:    str							=							'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
												SCREAMING_SNAKE_CASE							:    Any							=							create_dummy_object('''FakeClass'''    ,		'''\'torch\''''						)
												self.assertEqual(UpperCamelCase__    ,		UpperCamelCase__						)



						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
												SCREAMING_SNAKE_CASE							:    Any							=							create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}						)
												self.assertEqual(dummy_files['''torch''']    ,		UpperCamelCase__						)






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)





