




from typing import Any

import numpy as np






class       A      :
     """simple docstring"""

     def __init__(     self	: Union[str, Any]						,	A__	: np.ndarray						,	A__	: int      )					->							None:
           # Check if RHS is negative
           if np.any(tableau[:, -1]						,	where=tableau[:, -1] < 0      ):
                 raise ValueError('RHS must be > 0'      )

           lowerCamelCase__   	=					tableau
           lowerCamelCase__	,		lowerCamelCase__   	=					tableau.shape

           # Number of decision variables x1, x2, x3...
           lowerCamelCase__   	=					n_vars

           # Number of artificial variables to be minimised
           lowerCamelCase__   	=					len(np.where(tableau[self.n_vars : -1] == -1      )[0]      )

           # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
           lowerCamelCase__   	=					(self.n_art_vars > 0) + 1

           # Number of slack variables added to make inequalities into equalities
           lowerCamelCase__   	=					self.n_rows - self.n_stages

           # Objectives for each stage
           lowerCamelCase__   	=					['max']

           # In two stage simplex, first minimise then maximise
           if self.n_art_vars:
                 self.objectives.append('min'      )

           lowerCamelCase__   	=					['']

           # Index of current pivot row and column
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None

           # Does objective row only contain (non)-negative values?
           lowerCamelCase__   	=					False




     @staticmethod
     def 			__snake_case (     *A__	: int      )					->							list[str]:
           if len(A__      ) != 3:
                 raise ValueError('Must provide n_vars, n_slack, and n_art_vars'      )

           if not all(x >= 0 and isinstance(A__						,	A__      ) for x in args      ):
                 raise ValueError('All arguments must be non-negative integers'      )

           # decision | slack | artificial
           lowerCamelCase__   	=					['x', 's', 'a']
           lowerCamelCase__   	=					[]
           for i in range(3      ):
                 for j in range(args[i]      ):
                       titles.append(string_starts[i] + str(j + 1      )      )
           titles.append('RHS'      )
           return titles




     def 			__snake_case (     self	: List[Any]						,	A__	: np.ndarray      )					->							tuple[Any, Any]:
           lowerCamelCase__   	=					self.objectives[-1]

           # Find entries of highest magnitude in objective rows
           lowerCamelCase__   	=					(objective == 'min') - (objective == 'max')
           lowerCamelCase__   	=					np.argmax(sign * tableau[0, : self.n_vars]      )

           # Choice is only valid if below 0 for maximise, and above for minimise
           if sign * self.tableau[0, col_idx] <= 0:
                 lowerCamelCase__   	=					True
                 return 0, 0

           # Pivot row is chosen as having the lowest quotient when elements of
           # the pivot column divide the right-hand side

           # Slice excluding the objective rows
           lowerCamelCase__   	=					slice(self.n_stages						,	self.n_rows      )

           # RHS
           lowerCamelCase__   	=					tableau[s, -1]

           # Elements of pivot column within slice
           lowerCamelCase__   	=					tableau[s, col_idx]

           # Array filled with nans
           lowerCamelCase__   	=					np.full(self.n_rows - self.n_stages						,	np.nan      )

           # If element in pivot column is greater than zeron_stages, return
           # quotient or nan otherwise
           lowerCamelCase__   	=					np.divide(A__						,	A__						,	out=A__						,	where=divisor > 0      )

           # Arg of minimum quotient excluding the nan values. n_stages is added
           # to compensate for earlier exclusion of objective columns
           lowerCamelCase__   	=					np.nanargmin(A__      ) + self.n_stages
           return row_idx, col_idx




     def 			__snake_case (     self	: Tuple						,	A__	: np.ndarray						,	A__	: int						,	A__	: int      )					->							np.ndarray:
           lowerCamelCase__   	=					tableau[row_idx].copy()

           lowerCamelCase__   	=					piv_row[col_idx]

           # Entry becomes 1
           piv_row *= 1 / piv_val

           # Variable in pivot column becomes basic, ie the only non-zero entry
           for idx, coeff in enumerate(tableau[:, col_idx]      ):
                 tableau[idx] += -coeff * piv_row
           lowerCamelCase__   	=					piv_row
           return tableau




     def 			__snake_case (     self	: Tuple						,	A__	: np.ndarray      )					->							np.ndarray:
           self.objectives.pop()

           if not self.objectives:
                 return tableau

           # Slice containing ids for artificial columns
           lowerCamelCase__   	=					slice(-self.n_art_vars - 1						,	-1      )

           # Delete the artificial variable columns
           lowerCamelCase__   	=					np.delete(A__						,	A__						,	axis=1      )

           # Delete the objective row of the first stage
           lowerCamelCase__   	=					np.delete(A__						,	0						,	axis=0      )

           lowerCamelCase__   	=					1
           self.n_rows -= 1
           lowerCamelCase__   	=					0
           lowerCamelCase__   	=					False
           return tableau




     def 			__snake_case (     self	: int      )					->							dict[Any, Any]:
           # Stop simplex algorithm from cycling.
           for _ in range(1_0_0      ):
                 # Completion of each stage removes an objective. If both stages
                 # are complete, then no objectives are left
                 if not self.objectives:
                       lowerCamelCase__   	=					self.generate_col_titles(
                           self.n_vars						,	self.n_slack						,	self.n_art_vars      )

                       # Find the values of each variable at optimal solution
                       return self.interpret_tableau(self.tableau						,	self.col_titles      )

                 lowerCamelCase__	,		lowerCamelCase__   	=					self.find_pivot(self.tableau      )

                 # If there are no more negative values in objective row
                 if self.stop_iter:
                       # Delete artificial variable columns and rows. Update attributes
                       lowerCamelCase__   	=					self.change_stage(self.tableau      )
                 else:
                       lowerCamelCase__   	=					self.pivot(self.tableau						,	A__						,	A__      )
           return {}




     def 			__snake_case (     self	: Tuple						,	A__	: np.ndarray						,	A__	: list[str]      )					->							dict[str, float]:
           lowerCamelCase__   	=					{'P': abs(tableau[0, -1]      )}

           for i in range(self.n_vars      ):
                 # Gives ids of nonzero entries in the ith column
                 lowerCamelCase__   	=					np.nonzero(tableau[:, i]      )
                 lowerCamelCase__   	=					len(nonzero[0]      )

                 # First entry in the nonzero ids
                 lowerCamelCase__   	=					nonzero[0][0]
                 lowerCamelCase__   	=					tableau[nonzero_rowidx, i]

                 # If there is only one nonzero value in column, which is one
                 if n_nonzero == nonzero_val == 1:
                       lowerCamelCase__   	=					tableau[nonzero_rowidx, -1]
                       lowerCamelCase__   	=					rhs_val

        # Check for basic variables
           for title in col_titles:
                 # Don't add RHS or slack variables to output dict
                 if title[0] not in "R-s-a":
                       output_dict.setdefault(A__						,	0      )
           return output_dict


if __name__ == "__main__":
      import doctest

      doctest.testmod()





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							List[Any]=0    )       ->      Dict:


      '''simple docstring'''




      if name is None:
            lowerCamelCase__   	=					None
      else:
            lowerCamelCase__   	=					'.' * max(0  ,					spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowerCamelCase__   	=					fmt.format(lowerCamelCase    )

      # Print and recurse (if needed).
      if isinstance(lowerCamelCase  ,					lowerCamelCase    ):
            if msg is not None:
                  print(lowerCamelCase    )
            for k in val.keys():
                  recursive_print(lowerCamelCase  ,					val[k]  ,					spaces + 2    )
      elif isinstance(lowerCamelCase  ,					torch.Tensor    ):
            print(lowerCamelCase  ,					':'  ,					val.size()    )
      else:
            print(lowerCamelCase  ,					':'  ,					lowerCamelCase    )

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any    )       ->      Optional[int]:


      '''simple docstring'''




      lowerCamelCase__   	=					param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCamelCase__   	=					(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      lowerCamelCase__   	=					param.transpose(0  ,					1    ).contiguous()
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      return param

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							int    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					config.n_positions
      lowerCamelCase__   	=					config.n_layer
      lowerCamelCase__   	=					config.vocab_size
      lowerCamelCase__   	=					config.n_head
      lowerCamelCase__   	=					config.n_embd // config.n_head

      lowerCamelCase__   	=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCamelCase__   	=					{
          'transformer.wte.weight': word_embeddings,
          'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
          'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
          'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
      }

      lowerCamelCase__   	=					'model.language_model.encoder.layers.'
      for i in range(lowerCamelCase    ):
            lowerCamelCase__   	=					torch.tril(torch.ones((n_positions, n_positions)  ,					dtype=torch.bool    )    )
            lowerCamelCase__   	=					causal_mask.view(1  ,					1  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					causal_mask
            lowerCamelCase__   	=					torch.tensor(-1e4  ,					dtype=torch.bfloataa    )

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala.transpose(0  ,					1    ).contiguous()

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

      # For LM head, transformers' wants the matrix to weight embeddings.
      lowerCamelCase__   	=					word_embeddings

      return sd_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					6_40_00
      lowerCamelCase__   	=					config_megatron['encoder_seq_length']
      lowerCamelCase__   	=					config_megatron['hidden_size']
      lowerCamelCase__   	=					config_megatron['num_layers']
      lowerCamelCase__   	=					config_megatron['num_attention_heads']
      lowerCamelCase__   	=					config_megatron['ffn_hidden_size']
      lowerCamelCase__   	=					'gelu'
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					config_megatron['layernorm_epsilon']  # 1e-5
      lowerCamelCase__   	=					config_megatron['init_method_std']  # 0.02
      lowerCamelCase__   	=					config_megatron['apply_query_key_layer_scaling']  # True
      lowerCamelCase__   	=					True
      lowerCamelCase__   	=					True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					0  # <unk>
      else:
            lowerCamelCase__   	=					2  # <s>
            lowerCamelCase__   	=					3  # <|endoftext|>
            lowerCamelCase__   	=					0  # <pad>

      return config_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Dict:


      '''simple docstring'''




      print(lowerCamelCase    )

      lowerCamelCase__   	=					args.checkpoint_path
      lowerCamelCase__   	=					args.save_path
      if isfile(lowerCamelCase    ):
            raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

      # Load the model.
      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )

      # Load the config.
      lowerCamelCase__   	=					checkpoint['hyper_parameters']['cfg']
      lowerCamelCase__   	=					GPTaConfig()
      lowerCamelCase__   	=					copy_config(config_hf=lowerCamelCase  ,					config_megatron=lowerCamelCase    )
      lowerCamelCase__   	=					['GPT2LMHeadModel']

      lowerCamelCase__   	=					checkpoint['state_dict']

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase    )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(lowerCamelCase  ,					lowerCamelCase    )

      lowerCamelCase__   	=					'GPTSw3Tokenizer'

      # Store the config to file.
      print('Saving config'    )
      config_hf.save_pretrained(lowerCamelCase    )

      # Store the state_dict to file.
      lowerCamelCase__   	=					os.path.join(lowerCamelCase  ,					'pytorch_model.bin'    )
      print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
      torch.save(lowerCamelCase  ,					lowerCamelCase    )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Optional[int]      =argparse.ArgumentParser()
      parser.add_argument(
          """--checkpoint_path""",
          type=str,
          required=True,
          help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
      )
      parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
      parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
      __SCREAMING_SNAKE_CASE     : Tuple      =parser.parse_args()
      main(_args)