






'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


a_ 		=       logging.get_logger(__name__)





def        _a(     UpperCamelCase__ :  Union[str, Any],		UpperCamelCase__ :  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Optional[Any]													=b.T
      SCREAMING_SNAKE_CASE__   : str													=np.sum(np.square(lowerCAmelCase_							),		axis=1							)
      SCREAMING_SNAKE_CASE__   : Union[str, Any]													=np.sum(np.square(lowerCAmelCase_							),		axis=0							)
      SCREAMING_SNAKE_CASE__   : Tuple													=np.matmul(lowerCAmelCase_,		lowerCAmelCase_							)
      SCREAMING_SNAKE_CASE__   : Dict													=aa[:, None] - 2 * ab + ba[None, :]
      return d





def        _a(     UpperCamelCase__ :  Optional[Any],		UpperCamelCase__ :  str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : int													=x.reshape(-1,		3							)
      SCREAMING_SNAKE_CASE__   : str													=squared_euclidean_distance(lowerCAmelCase_,		lowerCAmelCase_							)
      return np.argmin(lowerCAmelCase_,		axis=1							)







class    __SCREAMING_SNAKE_CASE					(	_lowerCamelCase ):
 snake_case_     							=							["""pixel_values"""]




 def __init__(					self		:							Any    ,					__lowercase		:							int = None    ,					__lowercase		:							Union[str, Any] = True    ,					__lowercase		:							Dict = None    ,					__lowercase		:							Any = PILImageResampling.BILINEAR    ,					__lowercase		:							Any = True    ,					__lowercase		:							Tuple = True    ,					**__lowercase		:							Optional[int]    ,					)		->  None:
       super().__init__(**a_					)
       SCREAMING_SNAKE_CASE__   : str													=size if size is not None else {"""height""": 2_56, """width""": 2_56}
       SCREAMING_SNAKE_CASE__   : str													=get_size_dict(a_					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=np.array(a_					) if clusters is not None else None
       SCREAMING_SNAKE_CASE__   : Dict													=do_resize
       SCREAMING_SNAKE_CASE__   : List[str]													=size
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=resample
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=do_normalize
       SCREAMING_SNAKE_CASE__   : Tuple													=do_color_quantize




 def    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							Dict    ,					__lowercase		:							Tuple    ,					__lowercase		:							int = PILImageResampling.BILINEAR    ,					__lowercase		:							List[Any] = None    ,					**__lowercase		:							Optional[int]    ,					)		->  np.ndarray:
       SCREAMING_SNAKE_CASE__   : str													=get_size_dict(a_					)
       if "height" not in size or "width" not in size:
             raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}"					)
       return resize(
           a_    ,					size=(size['''height'''], size['''width'''])    ,					resample=a_    ,					data_format=a_    ,					**a_					)




 def    __magic_name__		(					self		:							Optional[Any]    ,					__lowercase		:							Dict    ,					__lowercase		:							List[Any] = None    ,					)		->  np.ndarray:
       SCREAMING_SNAKE_CASE__   : List[str]													=rescale(image=a_    ,					scale=1 / 127.5    ,					data_format=a_					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=image - 1
       return image




 def    __magic_name__		(					self		:							Optional[Any]    ,					__lowercase		:							int    ,					__lowercase		:							Tuple = None    ,					__lowercase		:							Any = None    ,					__lowercase		:							List[str] = None    ,					__lowercase		:							Union[str, Any] = None    ,					__lowercase		:							int = None    ,					__lowercase		:							List[str] = None    ,					__lowercase		:							Union[str, Any] = None    ,					__lowercase		:							Optional[Any] = ChannelDimension.FIRST    ,					**__lowercase		:							str    ,					)		->  PIL.Image.Image:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__   : List[str]													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__   : Dict													=get_size_dict(a_					)
       SCREAMING_SNAKE_CASE__   : List[str]													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__   : str													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__   : str													=do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       SCREAMING_SNAKE_CASE__   : Dict													=clusters if clusters is not None else self.clusters
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=np.array(a_					)

       SCREAMING_SNAKE_CASE__   : List[Any]													=make_list_of_images(a_					)

       if not valid_images(a_					):
             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 or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

       if do_color_quantize and clusters is None:
             raise ValueError('''Clusters must be specified if do_color_quantize is True.'''					)

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__   : Dict													=[to_numpy_array(a_					) for image in images]

       if do_resize:
             SCREAMING_SNAKE_CASE__   : Optional[int]													=[self.resize(image=a_    ,					size=a_    ,					resample=a_					) for image in images]

       if do_normalize:
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=[self.normalize(image=a_					) for image in images]

       if do_color_quantize:
             SCREAMING_SNAKE_CASE__   : List[Any]													=[to_channel_dimension_format(a_    ,					ChannelDimension.LAST					) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=np.array(a_					)
             SCREAMING_SNAKE_CASE__   : int													=color_quantize(a_    ,					a_					).reshape(images.shape[:-1]					)

             # flatten to (batch_size, height*width)
             SCREAMING_SNAKE_CASE__   : Any													=images.shape[0]
             SCREAMING_SNAKE_CASE__   : List[Any]													=images.reshape(a_    ,					-1					)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             SCREAMING_SNAKE_CASE__   : Dict													=list(a_					)
       else:
             SCREAMING_SNAKE_CASE__   : Any													=[to_channel_dimension_format(a_    ,					a_					) for image in images]

       SCREAMING_SNAKE_CASE__   : Tuple													={"""input_ids""": images}
       return BatchFeature(data=a_    ,					tensor_type=a_					)




'''simple docstring'''






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_							: Union[str, Any]						 =	logging.get_logger(__name__)

A_							: Union[str, Any]						 =	[
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

A_							: int						 =	[
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]




def     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)
       return sd




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=rename_keys_prefix		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = OrderedDict()
       _UpperCAmelCase  :						Any			     = torch.arange(config.max_position_embeddings		).expand((1, -1)		)
       # detector_d = OrderedDict()
       for key in d:
              if "detector" in key:
                     # detector_d[key.replace('detector.','')] = d[key]
                     continue
              _UpperCAmelCase  :						Dict			     = key
              for name_pair in rename_keys_prefix:
                     _UpperCAmelCase  :						str			     = new_key.replace(name_pair[0]       ,   name_pair[1]		)
              _UpperCAmelCase  :						str			     = d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     _UpperCAmelCase  :						int			     = new_d["""cls.predictions.bias"""]
       return new_d




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       assert (
           checkpoint_path.split("""/"""		)[-1] in ACCEPTABLE_CHECKPOINTS
       ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

       # Get Config
       if "pre" in checkpoint_path:
              _UpperCAmelCase  :						Optional[int]			     = """pretraining"""
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						Optional[int]			     = {"""visual_embedding_dim""": 512}
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						List[Any]			     = {"""visual_embedding_dim""": 2048}
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 2048}
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 1024}
              else:
                     raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''		)
       else:
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 512}
                     _UpperCAmelCase  :						int			     = """multichoice"""
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 2048}
                     _UpperCAmelCase  :						int			     = """vqa_advanced"""
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						List[str]			     = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
                     _UpperCAmelCase  :						int			     = """vqa"""
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						int			     = {
                         """visual_embedding_dim""": 1024,
                         """num_labels""": 2,
                     }
                     _UpperCAmelCase  :						Optional[Any]			     = """nlvr"""

       _UpperCAmelCase  :						int			     = VisualBertConfig(**lowerCAmelCase_		)

       # Load State Dict
       _UpperCAmelCase  :						Any			     = load_state_dict(lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[int]			     = get_new_dict(lowerCAmelCase_       ,   lowerCAmelCase_		)

       if model_type == "pretraining":
              _UpperCAmelCase  :						List[str]			     = VisualBertForPreTraining(lowerCAmelCase_		)
       elif model_type == "vqa":
              _UpperCAmelCase  :						Optional[int]			     = VisualBertForQuestionAnswering(lowerCAmelCase_		)
       elif model_type == "nlvr":
              _UpperCAmelCase  :						str			     = VisualBertForVisualReasoning(lowerCAmelCase_		)
       elif model_type == "multichoice":
              _UpperCAmelCase  :						Dict			     = VisualBertForMultipleChoice(lowerCAmelCase_		)

       model.load_state_dict(lowerCAmelCase_		)
       # Save Checkpoints
       Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
       model.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
   A_							: int						 =	parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



