




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase       :	Optional[int]									=     2







class 						A:



 '''simple docstring'''


 def __init__(       self      :						Tuple		,							*,  # begin keyword-only arguments
     A_      :						Optional[Any]="<s>"		,							A_      :						Tuple="<pad>"		,							A_      :						Any="</s>"		,							A_      :						Optional[int]="<unk>"		,							A_      :						int=None		,							)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					bos, unk, pad, eos
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					{}
       lowerCamelCase_	  =					self.add_symbol(A_						)
       lowerCamelCase_	  =					self.add_symbol(A_						)
       lowerCamelCase_	  =					self.add_symbol(A_						)
       lowerCamelCase_	  =					self.add_symbol(A_						)
       if extra_special_symbols:
             for s in extra_special_symbols:
                   self.add_symbol(A_						)
       lowerCamelCase_	  =					len(self.symbols						)

 def __eq__(       self      :						str		,							A_      :						Dict						)     ->			str:

       """simple docstring"""






       return self.indices == other.indices

 def __getitem__(       self      :						Tuple		,							A_      :						List[Any]						)     ->			Any:

       """simple docstring"""






       if idx < len(self.symbols						):
             return self.symbols[idx]
       return self.unk_word

 def __len__(       self      :						List[str]						)     ->			Union[str, Any]:

       """simple docstring"""






       return len(self.symbols						)

 def __contains__(       self      :						Any		,							A_      :						Dict						)     ->			Union[str, Any]:

       """simple docstring"""






       return sym in self.indices

 @classmethod
 def        a__				(       cls      :						Dict		,							A_      :						List[str]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					cls()
       d.add_from_file(A_						)
       return d

 def        a__				(       self      :						Any		,							A_      :						Tuple		,							A_      :						Tuple=1		,							A_      :						Tuple=False						)     ->			Dict:

       """simple docstring"""






       if word in self.indices and not overwrite:
             lowerCamelCase_	  =					self.indices[word]
             lowerCamelCase_	  =					self.count[idx] + n
             return idx
       else:
             lowerCamelCase_	  =					len(self.symbols						)
             lowerCamelCase_	  =					idx
             self.symbols.append(A_						)
             self.count.append(A_						)
             return idx

 def        a__				(       self      :						int		,							A_      :						List[Any]						)     ->			Optional[int]:

       """simple docstring"""






       return 0





 def        a__				(       self      :						Dict		,							A_      :						Optional[Any]						)     ->			Optional[int]:

       """simple docstring"""






       if isinstance(A_		,							A_						):
             try:
                   with open(A_		,							'r'		,							encoding='utf-8'						) as fd:
                         self.add_from_file(A_						)
             except FileNotFoundError as fnfe:
                   raise fnfe
             except UnicodeError:
                   raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(A_						)						)
             return

       lowerCamelCase_	  =					f.readlines()
       lowerCamelCase_	  =					self._load_meta(A_						)

       for line in lines[indices_start_line:]:
             try:
                   lowerCamelCase_  ,       lowerCamelCase_	  =					line.rstrip().rsplit(' '		,							1						)
                   if field == "#fairseq:overwrite":
                         lowerCamelCase_	  =					True
                         lowerCamelCase_  ,       lowerCamelCase_	  =					line.rsplit(' '		,							1						)
                   else:
                         lowerCamelCase_	  =					False
                   lowerCamelCase_	  =					int(A_						)
                   lowerCamelCase_	  =					line
                   if word in self and not overwrite:
                         raise RuntimeError(
                             'Duplicate word found when loading Dictionary: \'{}\'. '
                             'Duplicate words can overwrite earlier ones by adding the '
                             '#fairseq:overwrite flag at the end of the corresponding row '
                             'in the dictionary file. If using the Camembert model, please '
                             'download an updated copy of the model file.'.format(A_						)						)
                   self.add_symbol(A_		,							n=A_		,							overwrite=A_						)
             except ValueError:
                   raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''						)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					dict((re.sub(r'@@$'			,							''			,							lowercase  ), v) if k.endswith('@@'  ) else (re.sub(r'$'			,							'</w>'			,							lowercase  ), v) for k, v in d.items()  )
      lowerCamelCase_	  =					'<s> <pad> </s> <unk>'.split()
      # restore the special tokens
      for k in keep_keys:
            del da[f"""{k}</w>"""]
            lowerCamelCase_	  =					d[k]  # restore
      return da

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



      if not os.path.exists(lowercase  ):
            raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""  )
      os.makedirs(lowercase			,							exist_ok=lowercase  )
      print(f"""Writing results to {pytorch_dump_folder_path}"""  )

      # handle various types of models

      lowerCamelCase_	  =					os.path.join(lowercase			,							'checkpoint.pt'  )
      if not os.path.isfile(lowercase  ):
            raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""  )
      lowerCamelCase_	  =					torch.load(lowercase			,							map_location='cpu'  )

      lowerCamelCase_	  =					chkpt['cfg']['model']

      # dicts
      lowerCamelCase_	  =					os.path.join(lowercase			,							'dict.txt'  )
      if not os.path.isfile(lowercase  ):
            raise ValueError(f"""path to the file {dict_file} does not exist!"""  )
      lowerCamelCase_	  =					Dictionary.load(lowercase  )
      lowerCamelCase_	  =					rewrite_dict_keys(src_dict.indices  )
      lowerCamelCase_	  =					len(lowercase  )
      lowerCamelCase_	  =					os.path.join(lowercase			,							VOCAB_FILES_NAMES['vocab_file']  )
      print(f"""Generating {src_vocab_file} of {src_vocab_size} records"""  )
      with open(lowercase			,							'w'			,							encoding='utf-8'  ) as f:
            f.write(json.dumps(lowercase			,							ensure_ascii=lowercase			,							indent=lowercase  )  )

      # merges_file (bpecodes)
      lowerCamelCase_	  =					os.path.join(lowercase			,							'bpecodes'  )
      if not os.path.isfile(lowercase  ):
            raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""  )

      lowerCamelCase_	  =					os.path.join(lowercase			,							VOCAB_FILES_NAMES['merges_file']  )
      shutil.copyfile(lowercase			,							lowercase  )

      # model config
      lowerCamelCase_	  =					os.path.join(lowercase			,							'config.json'  )

      lowerCamelCase_	  =					{
          'activation_dropout': args['activation_dropout'],
          'architectures': ['BioGptForCausalLM'],
          'attention_probs_dropout_prob': args['attention_dropout'],
          'bos_token_id': 0,
          'eos_token_id': 2,
          'hidden_act': args['activation_fn'],
          'hidden_dropout_prob': args['dropout'],
          'hidden_size': args['decoder_embed_dim'],
          'initializer_range': 0.02,
          'intermediate_size': args['decoder_ffn_embed_dim'],
          'layer_norm_eps': 1e-1_2,
          'layerdrop': args['decoder_layerdrop'],
          'max_position_embeddings': args['max_target_positions'],
          'model_type': 'biogpt',
          'num_attention_heads': args['decoder_attention_heads'],
          'num_hidden_layers': args['decoder_layers'],
          'pad_token_id': 1,
          'scale_embedding': not args['no_scale_embedding'],
          'tie_word_embeddings': args['share_decoder_input_output_embed'],
          'vocab_size': src_vocab_size,
      }

      # good hparam defaults to start with

      print(f"""Generating {biogpt_model_config_file}"""  )
      with open(lowercase			,							'w'			,							encoding='utf-8'  ) as f:
            f.write(json.dumps(lowercase			,							ensure_ascii=lowercase			,							indent=lowercase  )  )

      # tokenizer config
      lowerCamelCase_	  =					os.path.join(lowercase			,							lowercase  )

      lowerCamelCase_	  =					{
          'bos_token': '<s>',
          'eos_token': '</s>',
          'model_max_length': 10_24,
          'pad_token': '<pad>',
          'special_tokens_map_file': None,
          'tokenizer_class': 'BioGptTokenizer',
          'unk_token': '<unk>',
      }

      print(f"""Generating {biogpt_tokenizer_config_file}"""  )
      with open(lowercase			,							'w'			,							encoding='utf-8'  ) as f:
            f.write(json.dumps(lowercase			,							ensure_ascii=lowercase			,							indent=lowercase  )  )

      # model
      lowerCamelCase_	  =					chkpt['model']

      # remove unneeded keys
      lowerCamelCase_	  =					[
          'decoder.version',
      ]
      for k in ignore_keys:
            model_state_dict.pop(lowercase			,							lowercase  )

      lowerCamelCase_	  =					list(model_state_dict.keys()  )
      for layer_name in layer_names:
            if layer_name.endswith('output_projection.weight'  ):
                  lowerCamelCase_	  =					model_state_dict.pop(lowercase  )
            else:
                  lowerCamelCase_	  =					model_state_dict.pop(lowercase  )

      lowerCamelCase_	  =					BioGptConfig.from_pretrained(lowercase  )
      lowerCamelCase_	  =					BioGptForCausalLM(lowercase  )

      # check that it loads ok
      model_new.load_state_dict(lowercase  )

      # save
      lowerCamelCase_	  =					os.path.join(lowercase			,							lowercase  )
      print(f"""Generating {pytorch_weights_dump_path}"""  )
      torch.save(lowercase			,							lowercase  )

      print('Conversion is done!'  )


if __name__ == "__main__":
   lowerCamelCase       :	Tuple									=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--biogpt_checkpoint_path",
       default=None,
       type=str,
       required=True,
       help=(
           "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
           " bpecodes, etc."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowerCamelCase       :	str									=     parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









from manim import *







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					Rectangle(height=0.5		,							width=0.5						)
       lowerCamelCase_	  =					Rectangle(height=0.46		,							width=0.46						).set_stroke(width=0						)

       lowerCamelCase_	  =					Rectangle(height=0.25		,							width=0.25						)

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

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

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

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

       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					[]

       for i, rect in enumerate(A_						):
             lowerCamelCase_	  =					fill.copy().set_fill(A_		,							opacity=0.8						)
             target.move_to(A_						)
             model_arr.append(A_						)

             lowerCamelCase_	  =					Rectangle(height=0.46		,							width=0.46						).set_stroke(width=0.0						).set_fill(A_		,							opacity=0.8						)
             cpu_target.move_to(cpu_left_col_base[i]						)
             model_cpu_arr.append(A_						)

       self.add(*A_		,							*A_						)

       lowerCamelCase_	  =					[meta_mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					[meta_mem.copy() for i in range(6						)]
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(*A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					VGroup(A_		,							A_						).arrange(A_		,							buff=0						)
       lowerCamelCase_	  =					Text('Disk'		,							font_size=24						)
       lowerCamelCase_	  =					Group(A_		,							A_						).arrange(A_		,							buff=0.5		,							aligned_edge=A_						)
       disk.move_to([-4, -1.25, 0]						)
       self.add(A_		,							A_						)

       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(A_		,							A_						)

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

       blue_text.next_to(A_		,							DOWN * 2.4		,							aligned_edge=key_text.get_left()						)
       self.add(A_						)

       lowerCamelCase_	  =					MarkupText(
           f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""		,							font_size=24		,							)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_						)						)

       lowerCamelCase_	  =					Square(0.3						)
       input.set_fill(A_		,							opacity=1.0						)
       input.set_stroke(width=0.0						)
       input.next_to(model_base[0]		,							A_		,							buff=0.5						)

       self.play(Write(A_						)						)

       input.generate_target()
       input.target.next_to(model_arr[0]		,							direction=A_		,							buff=0.02						)
       self.play(MoveToTarget(A_						)						)

       self.play(FadeOut(A_						)						)

       lowerCamelCase_	  =					Arrow(start=A_		,							end=A_		,							color=A_		,							buff=0.5						)
       a.next_to(model_arr[0].get_left()		,							A_		,							buff=0.2						)

       model_cpu_arr[0].generate_target()
       model_cpu_arr[0].target.move_to(gpu_rect[0]						)

       lowerCamelCase_	  =					MarkupText(
           f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""		,							font_size=24		,							)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_		,							run_time=3						)						)

       lowerCamelCase_	  =					{'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

       self.play(
           Write(A_						)		,							Circumscribe(model_arr[0]		,							color=A_		,							**A_						)		,							Circumscribe(model_cpu_arr[0]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							)
       self.play(MoveToTarget(model_cpu_arr[0]						)						)

       lowerCamelCase_	  =					a.copy()
       for i in range(6						):
             a_c.next_to(model_arr[i].get_right() + 0.02		,							A_		,							buff=0.2						)

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02						)

             lowerCamelCase_	  =					AnimationGroup(
                 FadeOut(A_		,							run_time=0.5						)		,							MoveToTarget(A_		,							run_time=0.5						)		,							FadeIn(A_		,							run_time=0.5						)		,							lag_ratio=0.2						)

             self.play(A_						)

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]						)

             if i < 5:
                   model_cpu_arr[i + 1].generate_target()
                   model_cpu_arr[i + 1].target.move_to(gpu_rect[0]						)
                   if i >= 1:
                         lowerCamelCase_	  =					0.7

                   self.play(
                       Circumscribe(model_arr[i]		,							**A_						)		,							Circumscribe(cpu_left_col_base[i]		,							**A_						)		,							Circumscribe(cpu_left_col_base[i + 1]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							Circumscribe(model_arr[i + 1]		,							color=A_		,							**A_						)		,							)
                   if i < 1:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]						)		,							MoveToTarget(model_cpu_arr[i + 1]						)		,							)
                   else:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]		,							run_time=0.7						)		,							MoveToTarget(model_cpu_arr[i + 1]		,							run_time=0.7						)		,							)
             else:
                   model_cpu_arr[i].generate_target()
                   model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]						)
                   input.generate_target()
                   input.target.next_to(model_arr[-1].get_right()		,							RIGHT + 0.02		,							buff=0.2						)

                   self.play(
                       Circumscribe(model_arr[-1]		,							color=A_		,							**A_						)		,							Circumscribe(cpu_left_col_base[-1]		,							color=A_		,							**A_						)		,							Circumscribe(gpu_rect[0]		,							color=A_		,							**A_						)		,							)

                   self.play(MoveToTarget(model_cpu_arr[i]						)						)

             lowerCamelCase_	  =					a_c
             lowerCamelCase_	  =					a_c.copy()

       input.generate_target()
       input.target.next_to(model_base[-1]		,							RIGHT + 0.02		,							buff=0.5						)
       self.play(
           FadeOut(A_						)		,							FadeOut(A_		,							run_time=0.5						)		,							)

       lowerCamelCase_	  =					MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""		,							font_size=24						)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(A_		,							run_time=3						)		,							MoveToTarget(A_						)						)

       self.wait()



