
'''simple docstring'''
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''EncodecFeatureExtractor'''
 lowerCamelCase				       =('''T5Tokenizer''', '''T5TokenizerFast''')
 def __init__(   self			:    str				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Dict       ):



   """simple docstring"""





   super().__init__(lowerCamelCase				,   lowerCamelCase       )
   __A		:     int												=		self.feature_extractor
   __A		:     List[Any]												=		False
 def 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Tuple=None				,   lowerCamelCase			:    str=True       ):



   """simple docstring"""





   return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase				,   language=lowerCamelCase				,   no_timestamps=lowerCamelCase       )
 def __call__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    str				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   if self._in_target_context_manager:
     return self.current_processor(*lowerCamelCase				,   **lowerCamelCase       )

   __A		:     List[str]												=		kwargs.pop("""audio"""				,   lowerCamelCase       )
   __A		:     Any												=		kwargs.pop("""sampling_rate"""				,   lowerCamelCase       )
   __A		:     Optional[int]												=		kwargs.pop("""text"""				,   lowerCamelCase       )
   if len(lowerCamelCase       ) > 0:
     __A		:     Optional[int]												=		args[0]
     __A		:     int												=		args[1:]

   if audio is None and text is None:
     raise ValueError("""You need to specify either an `audio` or `text` input to process."""       )

   if text is not None:
     __A		:     Dict												=		self.tokenizer(lowerCamelCase				,   **lowerCamelCase       )

   if audio is not None:
     __A		:     Optional[Any]												=		self.feature_extractor(lowerCamelCase				,   *lowerCamelCase				,   sampling_rate=lowerCamelCase				,   **lowerCamelCase       )

   if audio is None:
     return inputs

   elif text is None:
     return audio_inputs

   else:
     __A		:     Any												=		audio_inputs["""input_values"""]
     if "padding_mask" in audio_inputs:
       __A		:     List[Any]												=		audio_inputs["""padding_mask"""]
     return inputs
 def 		lowercase_(   self			:    List[str]				,   *lowerCamelCase			:    Tuple				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		kwargs.pop("""audio"""				,   lowerCamelCase       )
   __A		:     int												=		kwargs.pop("""padding_mask"""				,   lowerCamelCase       )

   if len(lowerCamelCase       ) > 0:
     __A		:     int												=		args[0]
     __A		:     Optional[Any]												=		args[1:]

   if audio_values is not None:
     return self._decode_audio(lowerCamelCase				,   padding_mask=lowerCamelCase       )
   else:
     return self.tokenizer.batch_decode(*lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]				,   *lowerCamelCase			:    Any				,   **lowerCamelCase			:    str       ):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase				,   **lowerCamelCase       )






 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Optional = None       ):



   """simple docstring"""





   __A		:     Any												=		to_numpy(lowerCamelCase       )
   __A							,					__A							,					__A		:     Optional[Any]												=		audio_values.shape

   if padding_mask is None:
     return list(lowerCamelCase       )

   __A		:     Optional[int]												=		to_numpy(lowerCamelCase       )

   # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
   # token (so that the generated audio values are **not** treated as padded tokens)
   __A		:     Dict												=		seq_len - padding_mask.shape[-1]
   __A		:     List[str]												=		1 - self.feature_extractor.padding_value
   __A		:     str												=		np.pad(lowerCamelCase				,   ((0, 0), (0, difference))				,   """constant"""				,   constant_values=lowerCamelCase       )

   __A		:     Tuple												=		audio_values.tolist()
   for i in range(lowerCamelCase       ):
     __A		:     int												=		np.asarray(audio_values[i]       )[
         padding_mask[i][None, :] != self.feature_extractor.padding_value
     ]
     __A		:     str												=		sliced_audio.reshape(lowerCamelCase				,   -1       )

   return audio_values





'''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_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       int:





  """simple docstring"""

  __A		:     Optional[int]												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
  __A		:     Tuple												=		Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw ).convert("""RGB""" )

  __A		:     Optional[Any]												=		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) ),
      ] )
  __A		:     Optional[int]												=		transform(__SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(__SCREAMING_SNAKE_CASE )
  return image







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       Optional[int]:





  """simple docstring"""

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

  if "encoder.pos_embed" in key:
    __A		:     Union[str, Any]												=		re.sub(R"""encoder.pos_embed"""			,     """embeddings.position_embedding"""			,     __SCREAMING_SNAKE_CASE )
  if "encoder.cls_token" in key:
    __A		:     Tuple												=		re.sub(R"""encoder.cls_token"""			,     """embeddings.class_embedding"""			,     __SCREAMING_SNAKE_CASE )

  if "self_attn" in key:
    __A		:     Tuple												=		re.sub(R"""self_attn.proj"""			,     """self_attn.projection"""			,     __SCREAMING_SNAKE_CASE )

  return key







@torch.no_grad()
def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  List[str]=None )				->       int:





  """simple docstring"""

  if config_path is not None:
    __A		:     Any												=		BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
  else:
    __A		:     List[Any]												=		BlipConfig(projection_dim=512			,     text_config={}			,     vision_config={} )

  __A		:     List[Any]												=		BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE ).eval()

  __A		:     List[str]												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

  __A		:     List[str]												=		blip_decoder(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=384			,     vit="""base""" )
  __A		:     List[str]												=		pt_model.eval()

  __A		:     int												=		pt_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     Tuple												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     Dict												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Tuple												=		value

  hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     List[Any]												=		384
  __A		:     Dict												=		load_demo_image(image_size=__SCREAMING_SNAKE_CASE			,     device="""cpu""" )
  __A		:     Dict												=		BertTokenizer.from_pretrained("""bert-base-uncased""" )
  __A		:     Optional[Any]												=		tokenizer(["""a picture of"""] ).input_ids

  __A		:     int												=		hf_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

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

  __A		:     str												=		hf_model.generate(__SCREAMING_SNAKE_CASE )

  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(__SCREAMING_SNAKE_CASE )

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

  __A		:     List[Any]												=		blip_vqa(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="""base""" )
  vqa_model.eval()

  __A		:     List[Any]												=		vqa_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     List[Any]												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     int												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Union[str, Any]												=		value

  __A		:     Any												=		BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE )

  hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     Tuple												=		["""How many dogs are in this image?"""]
  __A		:     Union[str, Any]												=		tokenizer(__SCREAMING_SNAKE_CASE			,     return_tensors="""pt""" ).input_ids

  __A		:     List[str]												=		hf_vqa_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_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""" )

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

  __A		:     List[str]												=		blip_itm(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="""base""" )
  itm_model.eval()

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

  __A		:     List[Any]												=		BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE )

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

  hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE )
  hf_itm_model.eval()

  __A		:     Any												=		hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )

  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__":
     A__   :		Tuple 		=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')
     A__   :		Any 		=parser.parse_args()

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



