"""simple docstring"""







import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_SCREAMING_SNAKE_CASE	:		str   =   logging.getLogger(__name__)



def       A__    (	)  ->							Optional[Any]:
   __lowerCamelCase    		=					argparse.ArgumentParser(description='PyTorch Transformer Language Model'						)
   parser.add_argument('--model_name'      , type=lowercase_      , default='transfo-xl-wt103'      , help='pretrained model name'						)
   parser.add_argument(
       '--split'      , type=lowercase_      , default='test'      , choices=['all', 'valid', 'test']      , help='which split to evaluate'						)
   parser.add_argument('--batch_size'      , type=lowercase_      , default=10      , help='batch size'						)
   parser.add_argument('--tgt_len'      , type=lowercase_      , default=128      , help='number of tokens to predict'						)
   parser.add_argument('--ext_len'      , type=lowercase_      , default=0      , help='length of the extended context'						)
   parser.add_argument('--mem_len'      , type=lowercase_      , default=1600      , help='length of the retained previous heads'						)
   parser.add_argument('--clamp_len'      , type=lowercase_      , default=1000      , help='max positional embedding index'						)
   parser.add_argument('--no_cuda'      , action='store_true'      , help='Do not use CUDA even though CUA is available'						)
   parser.add_argument('--work_dir'      , type=lowercase_      , required=lowercase_      , help='path to the work_dir'						)
   parser.add_argument('--no_log'      , action='store_true'      , help='do not log the eval result'						)
   parser.add_argument('--same_length'      , action='store_true'      , help='set same length attention with masking'						)
   parser.add_argument('--server_ip'      , type=lowercase_      , default=''      , help='Can be used for distant debugging.'						)
   parser.add_argument('--server_port'      , type=lowercase_      , default=''      , help='Can be used for distant debugging.'						)
   __lowerCamelCase    		=					parser.parse_args()
   assert args.ext_len >= 0, "extended context length must be non-negative"

   if args.server_ip and args.server_port:
      # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
      import ptvsd

      print('Waiting for debugger attach'						)
      ptvsd.enable_attach(address=(args.server_ip, args.server_port)      , redirect_output=lowercase_						)
      ptvsd.wait_for_attach()

   __lowerCamelCase    		=					torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'						)
   logger.info('device: {}'.format(lowercase_						)						)

   # Load a pre-processed dataset
   # You can also build the corpus yourself using TransfoXLCorpus methods
   # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
   # and tokenizing the dataset
   # The pre-processed corpus is a convertion (using the conversion script )
   __lowerCamelCase    		=					TransfoXLCorpus.from_pretrained(args.model_name						)

   __lowerCamelCase    		=					corpus.get_iterator('valid'      , args.batch_size      , args.tgt_len      , device=lowercase_      , ext_len=args.ext_len						)
   __lowerCamelCase    		=					corpus.get_iterator('test'      , args.batch_size      , args.tgt_len      , device=lowercase_      , ext_len=args.ext_len						)

   # Load a pre-trained model
   __lowerCamelCase    		=					TransfoXLLMHeadModel.from_pretrained(args.model_name						)
   model.to(lowercase_						)

   logger.info(
       'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
           args.batch_size      , args.tgt_len      , args.ext_len      , args.mem_len      , args.clamp_len						)						)

   model.reset_memory_length(args.mem_len						)
   if args.clamp_len > 0:
      __lowerCamelCase    		=					args.clamp_len
   if args.same_length:
      __lowerCamelCase    		=					True

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(lowercase_     :	Optional[int]						):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      __lowerCamelCase		,__lowerCamelCase    		=					0, 0.0
      __lowerCamelCase    		=					time.time()
      with torch.no_grad():
         __lowerCamelCase    		=					None
         for idx, (data, target, seq_len) in enumerate(lowercase_						):
            __lowerCamelCase    		=					model(lowercase_      , lm_labels=lowercase_      , mems=lowercase_						)
            __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					ret
            __lowerCamelCase    		=					loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         __lowerCamelCase    		=					time.time() - start_time
      logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(lowercase_      , 1000 * total_time / (idx + 1)						)						)
      return total_loss / total_len

   # Run on test data.
   if args.split == "all":
      __lowerCamelCase    		=					evaluate(lowercase_						)
      __lowerCamelCase    		=					evaluate(lowercase_						)
   elif args.split == "valid":
      __lowerCamelCase    		=					evaluate(lowercase_						)
      __lowerCamelCase    		=					None
   elif args.split == "test":
      __lowerCamelCase    		=					evaluate(lowercase_						)
      __lowerCamelCase    		=					None

   def format_log(lowercase_     :	Tuple      , lowercase_     :	Dict						):
      __lowerCamelCase    		=					'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowercase_      , lowercase_      , math.exp(lowercase_						)						)
      return log_str

   __lowerCamelCase    		=					''
   if valid_loss is not None:
      log_str += format_log(lowercase_      , 'valid'						)
   if test_loss is not None:
      log_str += format_log(lowercase_      , 'test'						)

   logger.info('=' * 100						)
   logger.info(lowercase_						)
   logger.info('=' * 100						)


if __name__ == "__main__":
     main()









"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			int			,						a__:			Dict=0      )					->      Any:




					"""simple docstring"""



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

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





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Tuple			,						a__:			List[Any]			,						a__:			List[str]      )					->      str:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Optional[int]      )					->      List[str]:




					"""simple docstring"""



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

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

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

										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala.transpose(0			,						1      ).contiguous()

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala

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

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

					return sd_hf





def 					__UpperCamelCase (     a__:			Any			,						a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



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

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

					return config_hf





def 					__UpperCamelCase (     a__:			Dict      )					->      Union[str, Any]:




					"""simple docstring"""



					print(a__      )

					__lowercase        =      args.checkpoint_path
					__lowercase        =      args.save_path
					if isfile(a__      ):
										raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )

					# Load the model.
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )

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

					__lowercase        =      checkpoint['''state_dict''']

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__      )

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

					__lowercase        =      '''GPTSw3Tokenizer'''

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

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


if __name__ == "__main__":
							a__			:						Union[str, 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__			:						Dict							=			parser.parse_args()
							main(_args)


