
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Dict	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   size if size is not None else {"""shortest_edge""": 2_56}
   lowercase__	: str     		=   get_size_dict(SCREAMING_SNAKE_CASE_       ,	default_to_square=SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
   lowercase__	: Any     		=   get_size_dict(SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   do_resize
   lowercase__	: str     		=   size
   lowercase__	: Any     		=   resample
   lowercase__	: Dict     		=   do_center_crop
   lowercase__	: Optional[int]     		=   crop_size
   lowercase__	: Optional[Any]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Optional[int]     		=   do_normalize
   lowercase__	: str     		=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowercase__	: List[str]     		=   image_std if image_std is not None else IMAGENET_STANDARD_STD


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   get_size_dict(SCREAMING_SNAKE_CASE_       ,	default_to_square=SCREAMING_SNAKE_CASE_)
   if "shortest_edge" not in size:
     raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
   lowercase__	: str     		=   get_resize_output_image_size(SCREAMING_SNAKE_CASE_       ,	size=size["""shortest_edge"""]       ,	default_to_square=SCREAMING_SNAKE_CASE_)
   return resize(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_       ,	resample=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Any     		=   get_size_dict(SCREAMING_SNAKE_CASE_)
   return center_crop(SCREAMING_SNAKE_CASE_       ,	size=(size["""height"""], size["""width"""])       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   return normalize(SCREAMING_SNAKE_CASE_       ,	mean=SCREAMING_SNAKE_CASE_       ,	std=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: int     		=   do_resize if do_resize is not None else self.do_resize
   lowercase__	: Dict     		=   size if size is not None else self.size
   lowercase__	: Optional[Any]     		=   get_size_dict(SCREAMING_SNAKE_CASE_       ,	default_to_square=SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   resample if resample is not None else self.resample
   lowercase__	: Tuple     		=   do_center_crop if do_center_crop is not None else self.do_center_crop
   lowercase__	: List[Any]     		=   crop_size if crop_size is not None else self.crop_size
   lowercase__	: int     		=   get_size_dict(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: str     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: List[str]     		=   do_normalize if do_normalize is not None else self.do_normalize
   lowercase__	: Union[str, Any]     		=   image_mean if image_mean is not None else self.image_mean
   lowercase__	: Optional[int]     		=   image_std if image_std is not None else self.image_std

   lowercase__	: Union[str, Any]     		=   make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True.""")

   if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True.""")

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: str     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_resize:
     lowercase__	: Tuple     		=   [self.resize(image=SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_       ,	resample=SCREAMING_SNAKE_CASE_) for image in images]

   if do_center_crop:
     lowercase__	: Any     		=   [self.center_crop(image=SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: Tuple     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_normalize:
     lowercase__	: Tuple     		=   [self.normalize(image=SCREAMING_SNAKE_CASE_       ,	mean=SCREAMING_SNAKE_CASE_       ,	std=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)








import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: int     		=   {}

  for key, value in state_dict.items():
    if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
      continue

    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mim_head.cls.predictions"""			,			"""mmm_image_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mlm_head.cls.predictions"""			,			"""mmm_text_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.itm_head.cls"""			,			"""itm_head""" )
    lowercase__	: Tuple     		=   key.replace("""heads.cmd.itm_head.pooler"""			,			"""itm_head.pooler""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.clip_head.logit_scale"""			,			"""flava.logit_scale""" )
    lowercase__	: Optional[int]     		=   key.replace("""heads.fairseq_mlm.cls.predictions"""			,			"""mlm_head""" )
    lowercase__	: List[Any]     		=   key.replace("""heads.imagenet.mim_head.cls.predictions"""			,			"""mim_head""" )
    lowercase__	: int     		=   key.replace("""mm_text_projection"""			,			"""flava.text_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_image_projection"""			,			"""flava.image_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""image_encoder.module"""			,			"""flava.image_model""" )
    lowercase__	: Any     		=   key.replace("""text_encoder.module"""			,			"""flava.text_model""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_encoder.module.encoder.cls_token"""			,			"""flava.multimodal_model.cls_token""" )
    lowercase__	: Tuple     		=   key.replace("""mm_encoder.module"""			,			"""flava.multimodal_model""" )
    lowercase__	: Any     		=   key.replace("""text_projection"""			,			"""flava.text_projection""" )
    lowercase__	: List[Any]     		=   key.replace("""image_projection"""			,			"""flava.image_projection""" )

    lowercase__	: str     		=   value.float()

  for key, value in codebook_state_dict.items():
    lowercase__	: Any     		=   value

  return upgrade

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=None )		->						Union[str, Any]:


  '''simple docstring'''






  if config_path is not None:
    lowercase__	: int     		=   FlavaConfig.from_pretrained(lowercase_ )
  else:
    lowercase__	: Optional[int]     		=   FlavaConfig()

  lowercase__	: List[Any]     		=   FlavaForPreTraining(lowercase_ ).eval()

  lowercase__	: Dict     		=   convert_dalle_checkpoint(lowercase_			,			lowercase_			,			save_checkpoint=lowercase_ )

  if os.path.exists(lowercase_ ):
    lowercase__	: Dict     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  else:
    lowercase__	: Dict     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )

  lowercase__	: int     		=   upgrade_state_dict(lowercase_			,			lowercase_ )
  hf_model.load_state_dict(lowercase_ )
  lowercase__	: Optional[int]     		=   hf_model.state_dict()
  lowercase__	: Optional[int]     		=   count_parameters(lowercase_ )
  lowercase__	: Any     		=   count_parameters(lowercase_ ) + count_parameters(lowercase_ )

  assert torch.allclose(lowercase_			,			lowercase_			,			atol=1E-3 )

  hf_model.save_pretrained(lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		int        = argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
     parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     lowerCamelCase__     :		List[str]        = parser.parse_args()

     convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






