






"""simple docstring"""





from typing import Any

import numpy as np
class  __lowerCamelCase     :

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

       UpperCamelCase__								=     tableau
       UpperCamelCase__							,    UpperCamelCase__								=     tableau.shape

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

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

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

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

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

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

       UpperCamelCase__								=     ['']

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

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

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

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

       # decision | slack | artificial
       UpperCamelCase__								=     ['x', 's', 'a']
       UpperCamelCase__								=     []
       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     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							tuple[Any, Any]:
       UpperCamelCase__								=     self.objectives[-1]

       # Find entries of highest magnitude in objective rows
       UpperCamelCase__								=     (objective == 'min') - (objective == 'max')
       UpperCamelCase__								=     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:
           UpperCamelCase__								=     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
       UpperCamelCase__								=     slice(self.n_stages			,					self.n_rows   )

       # RHS
       UpperCamelCase__								=     tableau[s, -1]

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

       # Array filled with nans
       UpperCamelCase__								=     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
       UpperCamelCase__								=     np.divide(snake_case_			,					snake_case_			,					out=snake_case_			,					where=divisor > 0   )

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

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							np.ndarray:
       UpperCamelCase__								=     tableau[row_idx].copy()

       UpperCamelCase__								=     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
       UpperCamelCase__								=     piv_row
       return tableau

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							np.ndarray:
       self.objectives.pop()

       if not self.objectives:
           return tableau

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

       # Delete the artificial variable columns
       UpperCamelCase__								=     np.delete(snake_case_			,					snake_case_			,					axis=1   )

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

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

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							dict[Any, Any]:
       # Stop simplex algorithm from cycling.
       for _ in range(100   ):
           # Completion of each stage removes an objective. If both stages
           # are complete, then no objectives are left
           if not self.objectives:
               UpperCamelCase__								=     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   )

           UpperCamelCase__							,    UpperCamelCase__								=     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
               UpperCamelCase__								=     self.change_stage(self.tableau   )
           else:
               UpperCamelCase__								=     self.pivot(self.tableau			,					snake_case_			,					snake_case_   )
       return {}







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							dict[str, float]:
       UpperCamelCase__								=     {'P': abs(tableau[0, -1]   )}

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

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

           # If there is only one nonzero value in column, which is one
           if n_nonzero == nonzero_val == 1:
               UpperCamelCase__								=     tableau[nonzero_rowidx, -1]
               UpperCamelCase__								=     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(snake_case_			,					0   )
       return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()












"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=0       )       ->						str:
    """simple docstring"""

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

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

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

    UpperCamelCase__								=     sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    UpperCamelCase__								=     {
        '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'],
    }

    UpperCamelCase__								=     'model.language_model.encoder.layers.'
    for i in range(SCREAMING_SNAKE_CASE       ):
        UpperCamelCase__								=     torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool       )       )
        UpperCamelCase__								=     causal_mask.view(1			,						1			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     causal_mask
        UpperCamelCase__								=     torch.tensor(-1E4			,						dtype=torch.bfloataa       )

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.bias']

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala.transpose(0			,						1       ).contiguous()

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

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

    return sd_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

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

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

    return config_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    print(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     args.checkpoint_path
    UpperCamelCase__								=     args.save_path
    if isfile(SCREAMING_SNAKE_CASE       ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'       )

    # Load the model.
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location='cpu'       )

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

    UpperCamelCase__								=     checkpoint['state_dict']

    # Convert.
    print('Converting'       )
    UpperCamelCase__								=     convert_megatron_checkpoint(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

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

    UpperCamelCase__								=     'GPTSw3Tokenizer'

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

    # Store the state_dict to file.
    UpperCamelCase__								=     os.path.join(SCREAMING_SNAKE_CASE			,						'pytorch_model.bin'       )
    print(F'Saving checkpoint to "{output_checkpoint_file}"'       )
    torch.save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  A__       :			Optional[Any]=						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""")
  A__       :			Any=						parser.parse_args()
  main(_args)




