





'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
A__    :       Dict              =logging.get_logger(__name__)
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							torch.load(lowerCAmelCase							,     map_location="""cpu"""  )
      if "model" in sd.keys():
            _lowerCAmelCase												=							torch.load(lowerCAmelCase							,     map_location="""cpu"""  )["""model"""]

      # pop unnecessary weights
      _lowerCAmelCase												=							[
          """decoder.version""",
          """decoder.output_projection.weight""",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(lowerCAmelCase  )

      _lowerCAmelCase												=							{
          """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
          """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
          """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
          """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _lowerCAmelCase												=							sd.pop(lowerCAmelCase  )

      _lowerCAmelCase												=							list(sd.keys()  )
      for key in keys:
            if ".qkv_proj." in key:
                  _lowerCAmelCase												=							sd[key]
                  # We split QKV in separate Q,K,V

                  _lowerCAmelCase												=							key.replace(""".qkv_proj."""							,     """.q_proj."""  )
                  _lowerCAmelCase												=							key.replace(""".qkv_proj."""							,     """.k_proj."""  )
                  _lowerCAmelCase												=							key.replace(""".qkv_proj."""							,     """.v_proj."""  )

                  _lowerCAmelCase												=							value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _lowerCAmelCase					,							_lowerCAmelCase					,							_lowerCAmelCase												=							torch.split(lowerCAmelCase							,     depth // 3							,     dim=0  )

                  _lowerCAmelCase												=							q
                  _lowerCAmelCase												=							k
                  _lowerCAmelCase												=							v
                  del sd[key]

      return sd
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None  ):





      """simple docstring"""


      _lowerCAmelCase												=							load_checkpoint(lowerCAmelCase  )

      if config is not None:
            _lowerCAmelCase												=							OPTConfig.from_pretrained(lowerCAmelCase  )
      else:
            _lowerCAmelCase												=							OPTConfig()

      _lowerCAmelCase												=							OPTModel(lowerCAmelCase  ).half().eval()
      model.load_state_dict(lowerCAmelCase  )

      # Check results
      Path(lowerCAmelCase  ).mkdir(exist_ok=lowerCAmelCase  )
      model.save_pretrained(lowerCAmelCase  )


if __name__ == "__main__":
      A__    :       Union[str, Any]              =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--fairseq_path''',
          type=str,
          help=(
              '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
              ''' https://huggingface.co/models?other=opt_metasq'''
          ),
      )
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
      A__    :       Tuple              =parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)













'''simple docstring'''






from manim import *




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				str:
            _lowerCAmelCase												=							Rectangle(height=0.5      ,      width=0.5  )
            _lowerCAmelCase												=							Rectangle(height=0.46      ,      width=0.46  ).set_stroke(width=0  )

            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							VGroup(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							Text("""CPU"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0.5      ,      aligned_edge=__snake_case  )
            cpu.move_to([-2.5, -0.5, 0]  )
            self.add(__snake_case  )

            _lowerCAmelCase												=							[mem.copy() for i in range(4  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							Text("""GPU"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0.5      ,      aligned_edge=__snake_case  )
            gpu.move_to([-1, -1, 0]  )
            self.add(__snake_case  )

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

            _lowerCAmelCase												=							Text("""Model"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0.5      ,      aligned_edge=__snake_case  )
            model.move_to([3, -1.0, 0]  )
            self.add(__snake_case  )

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

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

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

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

            _lowerCAmelCase												=							Text("""Loaded Checkpoint"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      aligned_edge=__snake_case      ,      buff=0.4  )
            checkpoint.move_to([3, 0.5, 0]  )

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

            _lowerCAmelCase												=							MarkupText(
                f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"      ,      font_size=18      ,      )

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

            self.add(__snake_case      ,      __snake_case  )

            _lowerCAmelCase												=							MarkupText(
                f"<span fgcolor='{BLUE}'>●</span> Checkpoint"      ,      font_size=18      ,      )

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

            _lowerCAmelCase												=							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=24      ,      )
            step_a.move_to([2, 2, 0]  )
            self.play(Write(__snake_case  )      ,      Write(__snake_case  )  )

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

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

                  _lowerCAmelCase												=							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(__snake_case      ,      run_time=1.5  )  )

            self.play(*__snake_case  )
            self.play(*__snake_case  )
            self.wait()






