
from __future__ import annotations

from typing import Any






class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   int       ,		snake_case__						:   float = 0					)						->      None:


          '''simple docstring'''


          snake_case							,   snake_case			:  List[Any]     							= row, column
          snake_case			:  Dict     							= [[default_value for c in range(snake_case__					)] for r in range(snake_case__					)]



   def __str__(self						:   Optional[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  List[Any]     							= f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

          # Make string identifier
          snake_case			:  List[Any]     							= 0
          for row_vector in self.array:
                 for obj in row_vector:
                        snake_case			:  Tuple     							= max(snake_case__       ,		len(str(snake_case__					)					)					)
          snake_case			:  List[Any]     							= f"""%{max_element_length}s"""

          # Make string and return
          def single_line(snake_case__						:   list[float]					) -> str:
                 nonlocal string_format_identifier
                 snake_case			:  str     							= "["
                 line += ", ".join(string_format_identifier % (obj,) for obj in row_vector					)
                 line += "]"
                 return line

          s += "\n".join(single_line(snake_case__					) for row_vector in self.array					)
          return s



   def __repr__(self						:   List[str]					)						->      str:


          '''simple docstring'''


          return str(self					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   tuple[int, int]					)						->      bool:


          '''simple docstring'''


          if not (isinstance(snake_case__       ,		(list, tuple)					) and len(snake_case__					) == 2):
                 return False
          elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
                 return False
          else:
                 return True



   def __getitem__(self						:   List[Any]       ,		snake_case__						:   tuple[int, int]					)						->      Any:


          '''simple docstring'''


          assert self.validate_indicies(snake_case__					)
          return self.array[loc[0]][loc[1]]



   def __setitem__(self						:   Tuple       ,		snake_case__						:   tuple[int, int]       ,		snake_case__						:   float					)						->      None:


          '''simple docstring'''


          assert self.validate_indicies(snake_case__					)
          snake_case			:  Optional[int]     							= value



   def __add__(self						:   str       ,		snake_case__						:   Matrix					)						->      Matrix:


          '''simple docstring'''


          assert isinstance(snake_case__       ,		snake_case__					)
          assert self.row == another.row and self.column == another.column

          # Add
          snake_case			:  Optional[int]     							= Matrix(self.row       ,		self.column					)
          for r in range(self.row					):
                 for c in range(self.column					):
                        snake_case			:  Dict     							= self[r, c] + another[r, c]
          return result



   def __neg__(self						:   Optional[Any]					)						->      Matrix:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= Matrix(self.row       ,		self.column					)
          for r in range(self.row					):
                 for c in range(self.column					):
                        snake_case			:  List[str]     							= -self[r, c]
          return result



   def __sub__(self						:   int       ,		snake_case__						:   Matrix					)						->      Matrix:


          '''simple docstring'''


          return self + (-another)



   def __mul__(self						:   List[str]       ,		snake_case__						:   int | float | Matrix					)						->      Matrix:


          '''simple docstring'''


          if isinstance(snake_case__       ,		(int, float)					):  # Scalar multiplication
                 snake_case			:  Tuple     							= Matrix(self.row       ,		self.column					)
                 for r in range(self.row					):
                        for c in range(self.column					):
                               snake_case			:  List[Any]     							= self[r, c] * another
                 return result
          elif isinstance(snake_case__       ,		snake_case__					):  # Matrix multiplication
                 assert self.column == another.row
                 snake_case			:  Union[str, Any]     							= Matrix(self.row       ,		another.column					)
                 for r in range(self.row					):
                        for c in range(another.column					):
                               for i in range(self.column					):
                                      result[r, c] += self[r, i] * another[i, c]
                 return result
          else:
                 snake_case			:  Union[str, Any]     							= f"""Unsupported type given for another ({type(snake_case__					)})"""
                 raise TypeError(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Matrix:


          '''simple docstring'''


          snake_case			:  int     							= Matrix(self.column       ,		self.row					)
          for r in range(self.row					):
                 for c in range(self.column					):
                        snake_case			:  Optional[Any]     							= self[r, c]
          return result



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Matrix       ,		snake_case__						:   Matrix					)						->      Any:


          '''simple docstring'''


          assert isinstance(snake_case__       ,		snake_case__					) and isinstance(snake_case__       ,		snake_case__					)
          assert self.row == self.column == u.row == v.row  # u, v should be column vector
          assert u.column == v.column == 1  # u, v should be column vector

          # Calculate
          snake_case			:  Optional[int]     							= v.transpose()
          snake_case			:  Optional[int]     							= (v_t * self * u)[0, 0] + 1
          if numerator_factor == 0:
                 return None  # It's not invertable
          return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


   def      UpperCamelCase   (       ):
          # a^(-1)
          snake_case			:  int     							= Matrix(3							,   3							,   0       )
          for i in range(3       ):
                 snake_case			:  Dict     							= 1
          print(f"""a^(-1) is {ainv}"""       )
          # u, v
          snake_case			:  Dict     							= Matrix(3							,   1							,   0       )
          snake_case							,   snake_case							,   snake_case			:  Dict     							= 1, 2, -3
          snake_case			:  List[str]     							= Matrix(3							,   1							,   0       )
          snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= 4, -2, 5
          print(f"""u is {u}"""       )
          print(f"""v is {v}"""       )
          print(f"""uv^T is {u * v.transpose()}"""       )
          # Sherman Morrison
          print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCamelCase							,   __lowerCamelCase       )}"""       )


   def      UpperCamelCase   (       ):
          import doctest

          doctest.testmod()




   testa()







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()
__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			[
    ("""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"""),
]

__lowerCamelCase				=			[
    """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      UpperCamelCase   (       __lowerCamelCase	:					int       ):
       snake_case			:  List[Any]     							= torch.load(__lowerCamelCase							,   map_location="cpu"       )
       return sd


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					Optional[Any]=rename_keys_prefix       ):
       snake_case			:  Optional[Any]     							= OrderedDict()
       snake_case			:  Optional[int]     							= 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
              snake_case			:  Any     							= key
              for name_pair in rename_keys_prefix:
                     snake_case			:  List[Any]     							= new_key.replace(name_pair[0]							,   name_pair[1]       )
              snake_case			:  List[str]     							= d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     snake_case			:  int     							= new_d["cls.predictions.bias"]
       return new_d


@torch.no_grad()
def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					Optional[Any]       ):
       assert (
           checkpoint_path.split("/"       )[-1] in ACCEPTABLE_CHECKPOINTS
       ), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

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

       snake_case			:  int     							= VisualBertConfig(**__lowerCamelCase       )

       # Load State Dict
       snake_case			:  Any     							= load_state_dict(__lowerCamelCase       )

       snake_case			:  List[Any]     							= get_new_dict(__lowerCamelCase							,   __lowerCamelCase       )

       if model_type == "pretraining":
              snake_case			:  str     							= VisualBertForPreTraining(__lowerCamelCase       )
       elif model_type == "vqa":
              snake_case			:  Any     							= VisualBertForQuestionAnswering(__lowerCamelCase       )
       elif model_type == "nlvr":
              snake_case			:  Optional[Any]     							= VisualBertForVisualReasoning(__lowerCamelCase       )
       elif model_type == "multichoice":
              snake_case			:  Optional[int]     							= VisualBertForMultipleChoice(__lowerCamelCase       )

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


if __name__ == "__main__":
   __lowerCamelCase				=			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.""")
   __lowerCamelCase				=			parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





