
'''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  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
      UpperCAmelCase_            =  Image.open(requests.get(UpperCamelCase__							,    stream=UpperCamelCase__					).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(UpperCamelCase__					).unsqueeze(0					).to(UpperCamelCase__					)
      return image



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


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

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

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

      return key



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[Any]=None					):


      '''simple docstring'''


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

      UpperCAmelCase_            =  BlipForConditionalGeneration(UpperCamelCase__					).eval()

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

      UpperCAmelCase_            =  blip_decoder(pretrained=UpperCamelCase__							,    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(UpperCamelCase__					)
            UpperCAmelCase_            =  rename_key(UpperCamelCase__					)
            UpperCAmelCase_            =  value

      hf_model.load_state_dict(UpperCamelCase__					)

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

      UpperCAmelCase_            =  hf_model.generate(UpperCamelCase__							,    UpperCamelCase__					)

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

      UpperCAmelCase_            =  hf_model.generate(UpperCamelCase__					)

      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(UpperCamelCase__					)

      # 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=UpperCamelCase__							,    image_size=UpperCamelCase__							,    vit='''base'''					)
      vqa_model.eval()

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

      UpperCAmelCase_            =  BlipForQuestionAnswering(UpperCamelCase__					)

      hf_vqa_model.load_state_dict(UpperCamelCase__					)

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

      UpperCAmelCase_            =  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'''					)

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

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

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

      UpperCAmelCase_            =  BlipForImageTextRetrieval(UpperCamelCase__					)

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

      UpperCAmelCase_            =  hf_itm_model(UpperCamelCase__							,    UpperCamelCase__							,    use_itm_head=UpperCamelCase__					)
      UpperCAmelCase_            =  hf_itm_model(UpperCamelCase__							,    UpperCamelCase__							,    use_itm_head=UpperCamelCase__					)

      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__":
      lowercase__  :      List[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__  :      Tuple        =					parser.parse_args()

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






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

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


lowercase__  :      int        =					logging.get_logger(__name__)

lowercase__  :      List[str]        =					{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''time_series_transformer'''
  lowerCAmelCase__										=	{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }






  def __init__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       str = "student_t"  ,							UpperCAmelCase__			:       str = "nll"  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							UpperCAmelCase__			:       Optional[Union[str, bool]] = "mean"  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "gelu"  ,							UpperCAmelCase__			:       int = 64  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							**UpperCAmelCase__			:       Tuple  ,							)				->Optional[int]:
        # time series specific configuration
        UpperCAmelCase_            =  prediction_length
        UpperCAmelCase_            =  context_length or prediction_length
        UpperCAmelCase_            =  distribution_output
        UpperCAmelCase_            =  loss
        UpperCAmelCase_            =  input_size
        UpperCAmelCase_            =  num_time_features
        UpperCAmelCase_            =  lags_sequence
        UpperCAmelCase_            =  scaling
        UpperCAmelCase_            =  num_dynamic_real_features
        UpperCAmelCase_            =  num_static_real_features
        UpperCAmelCase_            =  num_static_categorical_features
        if cardinality and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The cardinality should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  cardinality
        else:
              UpperCAmelCase_            =  [0]
        if embedding_dimension and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  embedding_dimension
        else:
              UpperCAmelCase_            =  [min(50  ,							(cat + 1) // 2  ) for cat in self.cardinality]
        UpperCAmelCase_            =  num_parallel_samples

        # Transformer architecture configuration
        UpperCAmelCase_            =  input_size * len(UpperCAmelCase__  ) + self._number_of_features
        UpperCAmelCase_            =  d_model
        UpperCAmelCase_            =  encoder_attention_heads
        UpperCAmelCase_            =  decoder_attention_heads
        UpperCAmelCase_            =  encoder_ffn_dim
        UpperCAmelCase_            =  decoder_ffn_dim
        UpperCAmelCase_            =  encoder_layers
        UpperCAmelCase_            =  decoder_layers

        UpperCAmelCase_            =  dropout
        UpperCAmelCase_            =  attention_dropout
        UpperCAmelCase_            =  activation_dropout
        UpperCAmelCase_            =  encoder_layerdrop
        UpperCAmelCase_            =  decoder_layerdrop

        UpperCAmelCase_            =  activation_function
        UpperCAmelCase_            =  init_std

        UpperCAmelCase_            =  use_cache

        super().__init__(is_encoder_decoder=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        return (
            sum(self.embedding_dimension  )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )





