

'''simple docstring'''




from manim import *



class 							lowercase_						(		a			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						Rectangle(height=0.5 ,			width=0.5  )
    UpperCAmelCase    				=						Rectangle(height=0.46 ,			width=0.46  ).set_stroke(width=0  )

    UpperCAmelCase    				=						[mem.copy() for i in range(6  )]
    UpperCAmelCase    				=						[mem.copy() for i in range(6  )]
    UpperCAmelCase    				=						VGroup(*a_  ).arrange(a_ ,			buff=0  )
    UpperCAmelCase    				=						VGroup(*a_  ).arrange(a_ ,			buff=0  )
    UpperCAmelCase    				=						VGroup(a_ ,			a_  ).arrange(a_ ,			buff=0  )
    UpperCAmelCase    				=						Text('CPU' ,			font_size=2_4  )
    UpperCAmelCase    				=						Group(a_ ,			a_  ).arrange(a_ ,			buff=0.5 ,			aligned_edge=a_  )
    cpu.move_to([-2.5, -0.5, 0]  )
    self.add(a_  )

    UpperCAmelCase    				=						[mem.copy() for i in range(4  )]
    UpperCAmelCase    				=						VGroup(*a_  ).arrange(a_ ,			buff=0  )
    UpperCAmelCase    				=						Text('GPU' ,			font_size=2_4  )
    UpperCAmelCase    				=						Group(a_ ,			a_  ).arrange(a_ ,			buff=0.5 ,			aligned_edge=a_  )
    gpu.move_to([-1, -1, 0]  )
    self.add(a_  )

    UpperCAmelCase    				=						[mem.copy() for i in range(6  )]
    UpperCAmelCase    				=						VGroup(*a_  ).arrange(a_ ,			buff=0  )

    UpperCAmelCase    				=						Text('Model' ,			font_size=2_4  )
    UpperCAmelCase    				=						Group(a_ ,			a_  ).arrange(a_ ,			buff=0.5 ,			aligned_edge=a_  )
    model.move_to([3, -1.0, 0]  )
    self.add(a_  )

    UpperCAmelCase    				=						[]
    for i, rect in enumerate(a_  ):
      rect.set_stroke(a_  )
      # target = fill.copy().set_fill(YELLOW, opacity=0.7)
      # target.move_to(rect)
      # self.add(target)

      UpperCAmelCase    				=						Rectangle(height=0.46 / 4 ,			width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(a_ ,			opacity=0.7  )

      if i == 0:
        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  ) ,			buff=0.02 ,			direction=a_  )
        cpu_target.set_x(cpu_target.get_x() + 0.1  )
      elif i == 3:
        cpu_target.next_to(cpu_targs[0] ,			direction=a_ ,			buff=0.0  )
      else:
        cpu_target.next_to(cpu_targs[i - 1] ,			direction=a_ ,			buff=0.0  )
      self.add(a_  )
      cpu_targs.append(a_  )

    UpperCAmelCase    				=						[mem.copy() for i in range(6  )]
    UpperCAmelCase    				=						VGroup(*a_  ).arrange(a_ ,			buff=0  )

    UpperCAmelCase    				=						Text('Loaded Checkpoint' ,			font_size=2_4  )
    UpperCAmelCase    				=						Group(a_ ,			a_  ).arrange(a_ ,			aligned_edge=a_ ,			buff=0.4  )
    checkpoint.move_to([3, 0.5, 0]  )

    UpperCAmelCase    				=						Square(side_length=2.2  )
    key.move_to([-5, 2, 0]  )

    UpperCAmelCase    				=						MarkupText(
        F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' ,			font_size=1_8 ,			)

    key_text.move_to([-5, 2.4, 0]  )

    self.add(a_ ,			a_  )

    UpperCAmelCase    				=						MarkupText(
        F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' ,			font_size=1_8 ,			)

    blue_text.next_to(a_ ,			DOWN * 2.4 ,			aligned_edge=key_text.get_left()  )

    UpperCAmelCase    				=						MarkupText(
        F'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' ,			font_size=2_4 ,			)
    step_a.move_to([2, 2, 0]  )
    self.play(Write(a_  ) ,			Write(a_  )  )

    self.play(Write(a_ ,			run_time=1  ) ,			Create(a_ ,			run_time=1  )  )

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						[]
    for i, rect in enumerate(a_  ):
      UpperCAmelCase    				=						fill.copy().set_fill(a_ ,			opacity=0.7  )
      target.move_to(a_  )
      first_animations.append(GrowFromCenter(a_ ,			run_time=1  )  )

      UpperCAmelCase    				=						target.copy()
      cpu_target.generate_target()
      if i < 5:
        cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
      else:
        cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
      second_animations.append(MoveToTarget(a_ ,			run_time=1.5  )  )

    self.play(*a_  )
    self.play(*a_  )
    self.wait()





'''simple docstring'''




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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  # 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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Optional[int]    ):
  UpperCAmelCase    				=						{}

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

    UpperCAmelCase    				=						key.replace('heads.cmd.mim_head.cls.predictions'							, 'mmm_image_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.mlm_head.cls.predictions'							, 'mmm_text_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.itm_head.cls'							, 'itm_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.itm_head.pooler'							, 'itm_head.pooler'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.clip_head.logit_scale'							, 'flava.logit_scale'    )
    UpperCAmelCase    				=						key.replace('heads.fairseq_mlm.cls.predictions'							, 'mlm_head'    )
    UpperCAmelCase    				=						key.replace('heads.imagenet.mim_head.cls.predictions'							, 'mim_head'    )
    UpperCAmelCase    				=						key.replace('mm_text_projection'							, 'flava.text_to_mm_projection'    )
    UpperCAmelCase    				=						key.replace('mm_image_projection'							, 'flava.image_to_mm_projection'    )
    UpperCAmelCase    				=						key.replace('image_encoder.module'							, 'flava.image_model'    )
    UpperCAmelCase    				=						key.replace('text_encoder.module'							, 'flava.text_model'    )
    UpperCAmelCase    				=						key.replace('mm_encoder.module.encoder.cls_token'							, 'flava.multimodal_model.cls_token'    )
    UpperCAmelCase    				=						key.replace('mm_encoder.module'							, 'flava.multimodal_model'    )
    UpperCAmelCase    				=						key.replace('text_projection'							, 'flava.text_projection'    )
    UpperCAmelCase    				=						key.replace('image_projection'							, 'flava.image_projection'    )

    UpperCAmelCase    				=						value.float()

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

  return upgrade


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Any=None    ):
  if config_path is not None:
    UpperCAmelCase    				=						FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE    )
  else:
    UpperCAmelCase    				=						FlavaConfig()

  UpperCAmelCase    				=						FlavaForPreTraining(SCREAMING_SNAKE_CASE    ).eval()

  UpperCAmelCase    				=						convert_dalle_checkpoint(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, save_checkpoint=SCREAMING_SNAKE_CASE    )

  if os.path.exists(SCREAMING_SNAKE_CASE    ):
    UpperCAmelCase    				=						torch.load(SCREAMING_SNAKE_CASE							, map_location='cpu'    )
  else:
    UpperCAmelCase    				=						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							, map_location='cpu'    )

  UpperCAmelCase    				=						upgrade_state_dict(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						hf_model.state_dict()
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    ) + count_parameters(SCREAMING_SNAKE_CASE    )

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

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE    )


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

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


