


'''simple docstring'''






import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]=0		)   ->  Optional[int]:




     '''simple docstring'''



     if name is None:
          _a			      =							None
     else:
          _a			      =							'.' * max(0				,						spaces - 2		) + '# {:' + str(50 - spaces		) + 's}'
          _a			      =							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 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Any:




     '''simple docstring'''



     _a			      =							param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          _a			      =							(num_heads, hidden_size, num_splits) + input_shape[1:]
          _a			      =							param.view(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						2		)
          _a			      =							param.transpose(1				,						2		).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          _a			      =							(num_heads, num_splits, hidden_size) + input_shape[1:]
          _a			      =							param.view(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						1		).contiguous()
     _a			      =							param.view(*lowerCAmelCase__		)
     return param






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{}

     # old versions did not store training args
     _a			      =							input_state_dict.get('args'				,						lowerCAmelCase__		)
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          _a			      =							ds_args.padded_vocab_size
          _a			      =							ds_args.max_position_embeddings
          _a			      =							ds_args.hidden_size
          _a			      =							ds_args.num_layers
          _a			      =							ds_args.num_attention_heads
          _a			      =							ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     _a			      =							config.n_head
     # The hidden_size per head.
     _a			      =							config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          _a			      =							input_state_dict['checkpoint_version']
     else:
          _a			      =							0.0

     # The model.
     _a			      =							input_state_dict['model']
     # The language model.
     _a			      =							model['language_model']
     # The embeddings.
     _a			      =							lm['embedding']

     # The word embeddings.
     _a			      =							embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     _a			      =							word_embeddings[: config.vocab_size, :]
     _a			      =							word_embeddings

     # The position embeddings.
     _a			      =							embeddings['position_embeddings']['weight']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     _a			      =							pos_embeddings.size(0		)
     if n_positions != config.n_positions:
          raise ValueError(
              f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'		)
     # Store the position embeddings.
     _a			      =							pos_embeddings

     # The transformer.
     _a			      =							lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

     # The regex to extract layer names.
     _a			      =							re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'		)

     # The simple map of names for "automated" rules.
     _a			      =							{
         'attention.dense': '.attn.c_proj.',
         'self_attention.dense': '.attn.c_proj.',
         'mlp.dense_h_to_4h': '.mlp.c_fc.',
         'mlp.dense_4h_to_h': '.mlp.c_proj.',
     }

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          _a			      =							layer_re.match(lowerCAmelCase__		)

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          _a			      =							int(m.group(1		)		)
          # The name of the operation.
          _a			      =							m.group(2		)
          # Is it a weight or a bias?
          _a			      =							m.group(3		)

          # The name of the layer.
          _a			      =							f'transformer.h.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'		):
               _a			      =							'ln_1' if op_name.startswith('input'		) else 'ln_2'
               _a			      =							val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Insert a tensor of 1x1xDxD bias.
               _a			      =							torch.tril(torch.ones((n_positions, n_positions)				,						dtype=torch.floataa		)		).view(
                   1				,						1				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               _a			      =							causal_mask

               # Insert a "dummy" tensor for masked_bias.
               _a			      =							torch.tensor(-1E4				,						dtype=torch.floataa		)
               _a			      =							masked_bias

               _a			      =							fix_query_key_value_ordering(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               _a			      =							out_val.transpose(0				,						1		).contiguous()
               # Store.
               _a			      =							out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               _a			      =							fix_query_key_value_ordering(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # Store. No change of shape.
               _a			      =							out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               _a			      =							megatron_to_transformers[op_name]
               _a			      =							val.transpose(0				,						1		)

          # Copy the bias.
          elif weight_or_bias == "bias":
               _a			      =							megatron_to_transformers[op_name]
               _a			      =							val

    # DEBUG.
     assert config.n_layer == layer_idx + 1

     # The final layernorm.
     _a			      =							transformer['final_layernorm.weight']
     _a			      =							transformer['final_layernorm.bias']

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

     # It should be done!
     return output_state_dict






def 	_A				()   ->  Any:




     '''simple docstring'''



     _a			      =							argparse.ArgumentParser()
     parser.add_argument('--print-checkpoint-structure'				,						action='store_true'		)
     parser.add_argument(
         'path_to_checkpoint'				,						type=lowerCAmelCase__				,						help='Path to the checkpoint file (.zip archive or direct .pt file)'				,						)
     parser.add_argument(
         '--config_file'				,						default=''				,						type=lowerCAmelCase__				,						help='An optional config json file describing the pre-trained model.'				,						)
     _a			      =							parser.parse_args()

     # Extract the basename.
     _a			      =							os.path.dirname(args.path_to_checkpoint		)

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'		)
     if args.path_to_checkpoint.endswith('.zip'		):
          with zipfile.ZipFile(args.path_to_checkpoint				,						'r'		) as checkpoint:
               with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'		) as pytorch_dict:
                    _a			      =							torch.load(lowerCAmelCase__				,						map_location='cpu'		)
     else:
          _a			      =							torch.load(args.path_to_checkpoint				,						map_location='cpu'		)

     _a			      =							input_state_dict.get('args'				,						lowerCAmelCase__		)

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    _a			      =							'gelu_fast'
               elif ds_args.openai_gelu:
                    _a			      =							'gelu_new'
               else:
                    _a			      =							'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               _a			      =							'gelu_new'

          # Spell out all parameters in case the defaults change.
          _a			      =							GPTaConfig(
              vocab_size=5_02_57				,						n_positions=10_24				,						n_embd=10_24				,						n_layer=24				,						n_head=16				,						n_inner=40_96				,						activation_function=lowerCAmelCase__				,						resid_pdrop=0.1				,						embd_pdrop=0.1				,						attn_pdrop=0.1				,						layer_norm_epsilon=1E-5				,						initializer_range=0.0_2				,						summary_type='cls_index'				,						summary_use_proj=lowerCAmelCase__				,						summary_activation=lowerCAmelCase__				,						summary_proj_to_labels=lowerCAmelCase__				,						summary_first_dropout=0.1				,						scale_attn_weights=lowerCAmelCase__				,						use_cache=lowerCAmelCase__				,						bos_token_id=5_02_56				,						eos_token_id=5_02_56				,						)
     else:
          _a			      =							GPTaConfig.from_json_file(args.config_file		)

     _a			      =							['GPT2LMHeadModel']

     # Convert.
     print('Converting'		)
     _a			      =							convert_megatron_checkpoint(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

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

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          _a			      =							ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               _a			      =							'gpt2'
          elif tokenizer_type == "PretrainedFromHF":
               _a			      =							ds_args.tokenizer_name_or_path
          else:
               raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'		)
     else:
          _a			      =							'gpt2'

     _a			      =							AutoTokenizer.from_pretrained(lowerCAmelCase__		)
     _a			      =							type(lowerCAmelCase__		).__name__
     _a			      =							tokenizer_class

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

     # Save tokenizer based on args
     print(f'Adding {tokenizer_class} tokenizer files'		)
     tokenizer.save_pretrained(lowerCAmelCase__		)

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


####################################################################################################

if __name__ == "__main__":
 main()

####################################################################################################





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Optional[int]:




     '''simple docstring'''



     print('\nThe shortest path matrix using Floyd Warshall algorithm\n'		)
     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               if dist[i][j] != float('inf'		):
                    print(int(dist[i][j]		)				,						end='\t'		)
               else:
                    print('INF'				,						end='\t'		)
          print()






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							[[float('inf'		) for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]

     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               _a			      =							graph[i][j]

               # check vertex k against all other vertices (i, j)
     for k in range(lowerCAmelCase__		):
          # looping through rows of graph array
          for i in range(lowerCAmelCase__		):
               # looping through columns of graph array
               for j in range(lowerCAmelCase__		):
                    if (
                        dist[i][k] != float('inf'		)
                        and dist[k][j] != float('inf'		)
                        and dist[i][k] + dist[k][j] < dist[i][j]
                    ):
                         _a			      =							dist[i][k] + dist[k][j]

     _print_dist(lowerCAmelCase__				,						lowerCAmelCase__		)
     return dist, v


if __name__ == "__main__":
 a_					:    Optional[Any]          =    int(input("Enter number of vertices: "))
 a_					:    List[Any]          =    int(input("Enter number of edges: "))

 a_					:    Dict          =    [[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  a_					:    Union[str, Any]          =    0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  a_					:    Union[str, Any]          =    int(input("Enter source:"))
  a_					:    int          =    int(input("Enter destination:"))
  a_					:    Tuple          =    float(input("Enter weight:"))
  a_					:    Dict          =    weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0

