"""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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							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)				),
							    ]				)
							A__	     =							transform(UpperCamelCase__				).unsqueeze(0				).to(UpperCamelCase__				)
							return image






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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

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

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

							return key






@torch.no_grad()
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=None				):






							"""simple docstring"""





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

							A__	     =							BlipForConditionalGeneration(UpperCamelCase__				).eval()

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

							A__	     =							blip_decoder(pretrained=UpperCamelCase__       ,    image_size=384       ,    vit='base'				)
							A__	     =							pt_model.eval()

							A__	     =							pt_model.state_dict()
							for key in modified_state_dict.copy():
														A__	     =							modified_state_dict.pop(UpperCamelCase__				)
														A__	     =							rename_key(UpperCamelCase__				)
														A__	     =							value

							hf_model.load_state_dict(UpperCamelCase__				)

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

							A__	     =							hf_model.generate(UpperCamelCase__       ,    UpperCamelCase__				)

							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]

							A__	     =							hf_model.generate(UpperCamelCase__				)

							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(UpperCamelCase__				)

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

							A__	     =							blip_vqa(pretrained=UpperCamelCase__       ,    image_size=UpperCamelCase__       ,    vit='base'				)
							vqa_model.eval()

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

							A__	     =							BlipForQuestionAnswering(UpperCamelCase__				)

							hf_vqa_model.load_state_dict(UpperCamelCase__				)

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

							A__	     =							hf_vqa_model.generate(UpperCamelCase__       ,    UpperCamelCase__				)
							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'				)

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

							A__	     =							blip_itm(pretrained=UpperCamelCase__       ,    image_size=UpperCamelCase__       ,    vit='base'				)
							itm_model.eval()

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

							A__	     =							BlipForImageTextRetrieval(UpperCamelCase__				)

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

							hf_itm_model.load_state_dict(UpperCamelCase__				)
							hf_itm_model.eval()

							A__	     =							hf_itm_model(UpperCamelCase__       ,    UpperCamelCase__       ,    use_itm_head=UpperCamelCase__				)
							A__	     =							hf_itm_model(UpperCamelCase__       ,    UpperCamelCase__       ,    use_itm_head=UpperCamelCase__				)

							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"""




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							(1 - _cos) / 2
							A__	     =							1 - _cos

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							(1 + _cos) / 2
							A__	     =							-1 - _cos

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							_sin / 2
							A__	     =							0
							A__	     =							-ba

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							1 - alpha
							A__	     =							-2 * _cos
							A__	     =							1 + alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)

							A__	     =							1 + alpha * big_a
							A__	     =							-2 * _cos
							A__	     =							1 - alpha * big_a
							A__	     =							1 + alpha / big_a
							A__	     =							-2 * _cos
							A__	     =							1 - alpha / big_a

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)
							A__	     =							(big_a + 1) - (big_a - 1) * _cos
							A__	     =							(big_a + 1) + (big_a - 1) * _cos
							A__	     =							(big_a - 1) - (big_a + 1) * _cos
							A__	     =							(big_a - 1) + (big_a + 1) * _cos
							A__	     =							2 * sqrt(UpperCamelCase__				) * alpha

							A__	     =							big_a * (pmc + aaa)
							A__	     =							2 * big_a * mpc
							A__	     =							big_a * (pmc - aaa)
							A__	     =							ppmc + aaa
							A__	     =							-2 * pmpc
							A__	     =							ppmc - aaa

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)
							A__	     =							(big_a + 1) - (big_a - 1) * _cos
							A__	     =							(big_a + 1) + (big_a - 1) * _cos
							A__	     =							(big_a - 1) - (big_a + 1) * _cos
							A__	     =							(big_a - 1) + (big_a + 1) * _cos
							A__	     =							2 * sqrt(UpperCamelCase__				) * alpha

							A__	     =							big_a * (ppmc + aaa)
							A__	     =							-2 * big_a * pmpc
							A__	     =							big_a * (ppmc - aaa)
							A__	     =							pmc + aaa
							A__	     =							2 * mpc
							A__	     =							pmc - aaa

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt





