

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  _A		(     snake_case					)      ->     Tuple:
   # encoder.embeddings are double copied in original FLAVA
   return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def  _A		(     snake_case		,							snake_case					)      ->     Union[str, Any]:
   _lowercase      :				List[str]      					=       {}

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

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

      _lowercase      :				Tuple      					=       value.float()

   for key, value in codebook_state_dict.items():
      _lowercase      :				Dict      					=       value

   return upgrade







@torch.no_grad()
def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case=None					)      ->     int:
   if config_path is not None:
      _lowercase      :				Any      					=       FlavaConfig.from_pretrained(snake_case					)
   else:
      _lowercase      :				Tuple      					=       FlavaConfig()

   _lowercase      :				Any      					=       FlavaForPreTraining(snake_case					).eval()

   _lowercase      :				Union[str, Any]      					=       convert_dalle_checkpoint(snake_case		,							snake_case		,							save_checkpoint=snake_case					)

   if os.path.exists(snake_case					):
      _lowercase      :				Tuple      					=       torch.load(snake_case		,							map_location="cpu"					)
   else:
      _lowercase      :				List[str]      					=       torch.hub.load_state_dict_from_url(snake_case		,							map_location="cpu"					)

   _lowercase      :				Tuple      					=       upgrade_state_dict(snake_case		,							snake_case					)
   hf_model.load_state_dict(snake_case					)
   _lowercase      :				int      					=       hf_model.state_dict()
   _lowercase      :				int      					=       count_parameters(snake_case					)
   _lowercase      :				Optional[int]      					=       count_parameters(snake_case					) + count_parameters(snake_case					)

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

   hf_model.save_pretrained(snake_case					)


if __name__ == "__main__":
    _snake_case         =  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')
    _snake_case         =  parser.parse_args()

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









'''simple docstring'''
from math import pi, sqrt, tan







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("surface_area_cube() only accepts non-negative values"					)
   return 6 * side_length**2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if length < 0 or breadth < 0 or height < 0:
      raise ValueError("surface_area_cuboid() only accepts non-negative values"					)
   return 2 * ((length * breadth) + (breadth * height) + (length * height))







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_sphere() only accepts non-negative values"					)
   return 4 * pi * radius**2







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_hemisphere() only accepts non-negative values"					)
   return 3 * pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cone() only accepts non-negative values"					)
   return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          "surface_area_conical_frustum() only accepts non-negative values"					)
   _lowercase      :				Union[str, Any]      					=       (height**2 + (radius_a - radius_a) ** 2) ** 0.5
   return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cylinder() only accepts non-negative values"					)
   return 2 * pi * radius * (height + radius)







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if torus_radius < 0 or tube_radius < 0:
      raise ValueError("surface_area_torus() only accepts non-negative values"					)
   if torus_radius < tube_radius:
      raise ValueError(
          "surface_area_torus() does not support spindle or self intersecting tori"					)
   return 4 * pow(snake_case		,							2					) * torus_radius * tube_radius







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if length < 0 or width < 0:
      raise ValueError("area_rectangle() only accepts non-negative values"					)
   return length * width







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("area_square() only accepts non-negative values"					)
   return side_length**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_triangle() only accepts non-negative values"					)
   return (base * height) / 2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if sidea < 0 or sidea < 0 or sidea < 0:
      raise ValueError("area_triangle_three_sides() only accepts non-negative values"					)
   elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
      raise ValueError("Given three sides do not form a triangle"					)
   _lowercase      :				Any      					=       (sidea + sidea + sidea) / 2
   _lowercase      :				List[str]      					=       sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)					)
   return area







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_parallelogram() only accepts non-negative values"					)
   return base * height







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if basea < 0 or basea < 0 or height < 0:
      raise ValueError("area_trapezium() only accepts non-negative values"					)
   return 1 / 2 * (basea + basea) * height







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("area_circle() only accepts non-negative values"					)
   return pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius_x < 0 or radius_y < 0:
      raise ValueError("area_ellipse() only accepts non-negative values"					)
   return pi * radius_x * radius_y







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if diagonal_a < 0 or diagonal_a < 0:
      raise ValueError("area_rhombus() only accepts non-negative values"					)
   return 1 / 2 * diagonal_a * diagonal_a







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if not isinstance(snake_case		,							snake_case					) or sides < 3:
      raise ValueError(
          "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"					)
   elif length < 0:
      raise ValueError(
          "area_reg_polygon() only accepts non-negative values as \
length of a side"					)
   return (sides * length**2) / (4 * tan(pi / sides					))
   return (sides * length**2) / (4 * tan(pi / sides					))


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

    print('[DEMO] Areas of various geometric shapes: \n')
    print(F'''Rectangle: {area_rectangle(10, 20) = }''')
    print(F'''Square: {area_square(10) = }''')
    print(F'''Triangle: {area_triangle(10, 10) = }''')
    print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
    print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
    print(F'''Rhombus: {area_rhombus(10, 20) = }''')
    print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
    print(F'''Circle: {area_circle(20) = }''')
    print(F'''Ellipse: {area_ellipse(10, 20) = }''')
    print('\nSurface Areas of various geometric shapes: \n')
    print(F'''Cube: {surface_area_cube(20) = }''')
    print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
    print(F'''Sphere: {surface_area_sphere(20) = }''')
    print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
    print(F'''Cone: {surface_area_cone(10, 20) = }''')
    print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
    print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
    print(F'''Torus: {surface_area_torus(20, 10) = }''')
    print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
    print(F'''Square: {area_reg_polygon(4, 10) = }''')
    print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')






