
'''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						:		Optional[int]		,     A						:		Optional[Any]):
							UpperCamelCase   			=     'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
							UpperCamelCase   			=     Image.open(requests.get(A		,     stream=A).raw).convert('RGB')

							UpperCamelCase   			=     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)),
							    ])
							UpperCamelCase   			=     transform(A).unsqueeze(0).to(A)
							return image







def 		A_(     A						:		Any):
							if "visual_encoder" in key:
														UpperCamelCase   			=     re.sub('visual_encoder*'		,     'vision_model.encoder'		,     A)
							if "blocks" in key:
														UpperCamelCase   			=     re.sub(r'blocks'		,     'layers'		,     A)
							if "attn" in key:
														UpperCamelCase   			=     re.sub(r'attn'		,     'self_attn'		,     A)
							if "norm1" in key:
														UpperCamelCase   			=     re.sub(r'norm1'		,     'layer_norm1'		,     A)
							if "norm2" in key:
														UpperCamelCase   			=     re.sub(r'norm2'		,     'layer_norm2'		,     A)
							if "encoder.norm" in key:
														UpperCamelCase   			=     re.sub(r'encoder.norm'		,     'post_layernorm'		,     A)
							if "encoder.patch_embed.proj" in key:
														UpperCamelCase   			=     re.sub(r'encoder.patch_embed.proj'		,     'embeddings.patch_embedding'		,     A)

							if "encoder.pos_embed" in key:
														UpperCamelCase   			=     re.sub(r'encoder.pos_embed'		,     'embeddings.position_embedding'		,     A)
							if "encoder.cls_token" in key:
														UpperCamelCase   			=     re.sub(r'encoder.cls_token'		,     'embeddings.class_embedding'		,     A)

							if "self_attn" in key:
														UpperCamelCase   			=     re.sub(r'self_attn.proj'		,     'self_attn.projection'		,     A)

							return key







@torch.no_grad()
def 		A_(     A						:		List[str]		,     A						:		Any=None):
							if config_path is not None:
														UpperCamelCase   			=     BlipConfig.from_pretrained(A)
							else:
														UpperCamelCase   			=     BlipConfig(projection_dim=512		,     text_config={}		,     vision_config={})

							UpperCamelCase   			=     BlipForConditionalGeneration(A).eval()

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

							UpperCamelCase   			=     blip_decoder(pretrained=A		,     image_size=384		,     vit='base')
							UpperCamelCase   			=     pt_model.eval()

							UpperCamelCase   			=     pt_model.state_dict()
							for key in modified_state_dict.copy():
														UpperCamelCase   			=     modified_state_dict.pop(A)
														UpperCamelCase   			=     rename_key(A)
														UpperCamelCase   			=     value

							hf_model.load_state_dict(A)

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

							UpperCamelCase   			=     hf_model.generate(A		,     A)

							assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

							UpperCamelCase   			=     hf_model.generate(A)

							assert out[0].tolist() == [3_0522, 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'
							UpperCamelCase   			=     (
							    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
							)

							UpperCamelCase   			=     blip_vqa(pretrained=A		,     image_size=A		,     vit='base')
							vqa_model.eval()

							UpperCamelCase   			=     vqa_model.state_dict()
							for key in modified_state_dict.copy():
														UpperCamelCase   			=     modified_state_dict.pop(A)
														UpperCamelCase   			=     rename_key(A)
														UpperCamelCase   			=     value

							UpperCamelCase   			=     BlipForQuestionAnswering(A)

							hf_vqa_model.load_state_dict(A)

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

							UpperCamelCase   			=     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')

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

							UpperCamelCase   			=     blip_itm(pretrained=A		,     image_size=A		,     vit='base')
							itm_model.eval()

							UpperCamelCase   			=     itm_model.state_dict()
							for key in modified_state_dict.copy():
														UpperCamelCase   			=     modified_state_dict.pop(A)
														UpperCamelCase   			=     rename_key(A)
														UpperCamelCase   			=     value

							UpperCamelCase   			=     BlipForImageTextRetrieval(A)

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

							UpperCamelCase   			=     hf_itm_model(A		,     A		,     use_itm_head=A)
							UpperCamelCase   			=     hf_itm_model(A		,     A		,     use_itm_head=A)

							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__":
						lowerCAmelCase					:    List[str]           = 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					:    Optional[Any]           = parser.parse_args()

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






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase					:    Optional[Any]           = {
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    str           = [
						    'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FalconForCausalLM',
						    'FalconModel',
						    'FalconPreTrainedModel',
						    'FalconForSequenceClassification',
						    'FalconForTokenClassification',
						    'FalconForQuestionAnswering',
						]


if TYPE_CHECKING:
						from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_falcon import (
												    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FalconForCausalLM,
												    FalconForQuestionAnswering,
												    FalconForSequenceClassification,
												    FalconForTokenClassification,
												    FalconModel,
												    FalconPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




