
'''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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     int													=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
						lowercase_	:     List[str]													=       Image.open(requests.get(__snake_case      ,      stream=__snake_case							).raw							).convert('''RGB'''							)

						lowercase_	:     Optional[int]													=       transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)      ,      interpolation=InterpolationMode.BICUBIC							),
						        transforms.ToTensor(),
						        transforms.Normalize((0.48145466, 0.4578275, 0.40821073)      ,      (0.26862954, 0.26130258, 0.27577711)							),
						    ]							)
						lowercase_	:     Union[str, Any]													=       transform(__snake_case							).unsqueeze(0							).to(__snake_case							)
						return image
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						if "visual_encoder" in key:
												lowercase_	:     List[str]													=       re.sub('''visual_encoder*'''      ,      '''vision_model.encoder'''      ,      __snake_case							)
						if "blocks" in key:
												lowercase_	:     Union[str, Any]													=       re.sub(R'''blocks'''      ,      '''layers'''      ,      __snake_case							)
						if "attn" in key:
												lowercase_	:     Tuple													=       re.sub(R'''attn'''      ,      '''self_attn'''      ,      __snake_case							)
						if "norm1" in key:
												lowercase_	:     Optional[int]													=       re.sub(R'''norm1'''      ,      '''layer_norm1'''      ,      __snake_case							)
						if "norm2" in key:
												lowercase_	:     Dict													=       re.sub(R'''norm2'''      ,      '''layer_norm2'''      ,      __snake_case							)
						if "encoder.norm" in key:
												lowercase_	:     List[Any]													=       re.sub(R'''encoder.norm'''      ,      '''post_layernorm'''      ,      __snake_case							)
						if "encoder.patch_embed.proj" in key:
												lowercase_	:     Any													=       re.sub(R'''encoder.patch_embed.proj'''      ,      '''embeddings.patch_embedding'''      ,      __snake_case							)

						if "encoder.pos_embed" in key:
												lowercase_	:     Dict													=       re.sub(R'''encoder.pos_embed'''      ,      '''embeddings.position_embedding'''      ,      __snake_case							)
						if "encoder.cls_token" in key:
												lowercase_	:     int													=       re.sub(R'''encoder.cls_token'''      ,      '''embeddings.class_embedding'''      ,      __snake_case							)

						if "self_attn" in key:
												lowercase_	:     List[Any]													=       re.sub(R'''self_attn.proj'''      ,      '''self_attn.projection'''      ,      __snake_case							)

						return key
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: int=None							):



						"""simple docstring"""





						if config_path is not None:
												lowercase_	:     Union[str, Any]													=       BlipConfig.from_pretrained(__snake_case							)
						else:
												lowercase_	:     Optional[Any]													=       BlipConfig(projection_dim=512      ,      text_config={}      ,      vision_config={}							)

						lowercase_	:     Tuple													=       BlipForConditionalGeneration(__snake_case							).eval()

						lowercase_	:     str													=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

						lowercase_	:     Tuple													=       blip_decoder(pretrained=__snake_case      ,      image_size=384      ,      vit='''base'''							)
						lowercase_	:     str													=       pt_model.eval()

						lowercase_	:     List[Any]													=       pt_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_	:     List[Any]													=       modified_state_dict.pop(__snake_case							)
												lowercase_	:     int													=       rename_key(__snake_case							)
												lowercase_	:     int													=       value

						hf_model.load_state_dict(__snake_case							)

						lowercase_	:     List[Any]													=       384
						lowercase_	:     Union[str, Any]													=       load_demo_image(image_size=__snake_case      ,      device='''cpu'''							)
						lowercase_	:     Union[str, Any]													=       BertTokenizer.from_pretrained('''bert-base-uncased'''							)
						lowercase_	:     Tuple													=       tokenizer(['''a picture of''']							).input_ids

						lowercase_	:     List[str]													=       hf_model.generate(__snake_case      ,      __snake_case							)

						assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

						lowercase_	:     str													=       hf_model.generate(__snake_case							)

						assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

						if pytorch_dump_folder_path is not None:
												hf_model.save_pretrained(__snake_case							)

						# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
						lowercase_	:     Optional[int]													=       (
						    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
						)

						lowercase_	:     List[Any]													=       blip_vqa(pretrained=__snake_case      ,      image_size=__snake_case      ,      vit='''base'''							)
						vqa_model.eval()

						lowercase_	:     Union[str, Any]													=       vqa_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_	:     Union[str, Any]													=       modified_state_dict.pop(__snake_case							)
												lowercase_	:     Optional[Any]													=       rename_key(__snake_case							)
												lowercase_	:     str													=       value

						lowercase_	:     List[str]													=       BlipForQuestionAnswering(__snake_case							)

						hf_vqa_model.load_state_dict(__snake_case							)

						lowercase_	:     Optional[int]													=       ["How many dogs are in this image?"]
						lowercase_	:     Any													=       tokenizer(__snake_case      ,      return_tensors='''pt'''							).input_ids

						lowercase_	:     int													=       hf_vqa_model.generate(__snake_case      ,      __snake_case							)
						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'''							)

						lowercase_	:     str													=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

						lowercase_	:     Any													=       blip_itm(pretrained=__snake_case      ,      image_size=__snake_case      ,      vit='''base'''							)
						itm_model.eval()

						lowercase_	:     Dict													=       itm_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_	:     Optional[Any]													=       modified_state_dict.pop(__snake_case							)
												lowercase_	:     List[str]													=       rename_key(__snake_case							)
												lowercase_	:     List[Any]													=       value

						lowercase_	:     Dict													=       BlipForImageTextRetrieval(__snake_case							)

						lowercase_	:     List[str]													=       ["A picture of a woman with a dog sitting in a beach"]
						lowercase_	:     List[str]													=       tokenizer(
						    __snake_case      ,      return_tensors='''pt'''      ,      padding='''max_length'''      ,      truncation=__snake_case      ,      max_length=35      ,      ).input_ids

						hf_itm_model.load_state_dict(__snake_case							)
						hf_itm_model.eval()

						lowercase_	:     Tuple													=       hf_itm_model(__snake_case      ,      __snake_case      ,      use_itm_head=__snake_case							)
						lowercase_	:     int													=       hf_itm_model(__snake_case      ,      __snake_case      ,      use_itm_head=__snake_case							)

						assert out[0].item() == 0.2110687494277954
						assert torch.nn.functional.softmax(out_itm[0]      ,      dim=1							)[:, 1].item() == 0.45698845386505127

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


if __name__ == "__main__":
							_lowercase :  Optional[Any]    							=       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")
							_lowercase :  List[str]    							=       parser.parse_args()

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








"""simple docstring"""






from ....configuration_utils import PretrainedConfig
from ....utils import logging


__snake_case							:   List[Any]      =    logging.get_logger(__name__)

__snake_case							:   Any      =    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'mctct'

	def __init__(     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		str=8065       ,			_SCREAMING_SNAKE_CASE:		str=1536       ,			_SCREAMING_SNAKE_CASE:		str=36       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=6144       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=4       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=384       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=920       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1e-5       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.3       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="relu"       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.02       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.3       ,			_SCREAMING_SNAKE_CASE:		Dict=0.3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=1       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0       ,			_SCREAMING_SNAKE_CASE:		List[str]=2       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.3       ,			_SCREAMING_SNAKE_CASE:		Dict=1       ,			_SCREAMING_SNAKE_CASE:		int=(7,)       ,			_SCREAMING_SNAKE_CASE:		str=(3,)       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=80       ,			_SCREAMING_SNAKE_CASE:		Tuple=1       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		Tuple="sum"       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			**_SCREAMING_SNAKE_CASE:		Tuple       ,			)				->       Tuple:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE       ,			pad_token_id=_SCREAMING_SNAKE_CASE       ,			bos_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       vocab_size
				__lowerCAmelCase      :  str												=       hidden_size
				__lowerCAmelCase      :  str												=       num_hidden_layers
				__lowerCAmelCase      :  str												=       intermediate_size
				__lowerCAmelCase      :  List[Any]												=       num_attention_heads
				__lowerCAmelCase      :  Dict												=       attention_head_dim
				__lowerCAmelCase      :  Optional[int]												=       max_position_embeddings
				__lowerCAmelCase      :  str												=       layer_norm_eps
				__lowerCAmelCase      :  Tuple												=       layerdrop
				__lowerCAmelCase      :  str												=       hidden_act
				__lowerCAmelCase      :  List[Any]												=       initializer_range
				__lowerCAmelCase      :  int												=       hidden_dropout_prob
				__lowerCAmelCase      :  Optional[Any]												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  str												=       pad_token_id
				__lowerCAmelCase      :  Optional[int]												=       bos_token_id
				__lowerCAmelCase      :  Union[str, Any]												=       eos_token_id
				__lowerCAmelCase      :  Any												=       conv_glu_dim
				__lowerCAmelCase      :  Optional[int]												=       conv_dropout
				__lowerCAmelCase      :  Union[str, Any]												=       num_conv_layers
				__lowerCAmelCase      :  Optional[int]												=       input_feat_per_channel
				__lowerCAmelCase      :  Union[str, Any]												=       input_channels
				__lowerCAmelCase      :  Optional[Any]												=       conv_channels
				__lowerCAmelCase      :  Dict												=       ctc_loss_reduction
				__lowerCAmelCase      :  int												=       ctc_zero_infinity

				# prevents config testing fail with exporting to json
				__lowerCAmelCase      :  List[str]												=       list(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       list(_SCREAMING_SNAKE_CASE)

				if len(self.conv_kernel) != self.num_conv_layers:
							raise ValueError(
							    "Configuration for convolutional module is incorrect. "
							    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
							    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, """
							    F"""`config.num_conv_layers = {self.num_conv_layers}`.""")