


"""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
)
snake_case__		:  str     					=					logging.getLogger(__name__)


def   _snake_case    (    ):
      lowerCAmelCase       :						Optional[int]							= argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''      )
      parser.add_argument('''--model_name'''			, type=_snake_case			, default='''transfo-xl-wt103'''			, help='''pretrained model name'''      )
      parser.add_argument(
          '''--split'''			, type=_snake_case			, default='''test'''			, choices=['''all''', '''valid''', '''test''']			, help='''which split to evaluate'''      )
      parser.add_argument('''--batch_size'''			, type=_snake_case			, default=10			, help='''batch size'''      )
      parser.add_argument('''--tgt_len'''			, type=_snake_case			, default=128			, help='''number of tokens to predict'''      )
      parser.add_argument('''--ext_len'''			, type=_snake_case			, default=0			, help='''length of the extended context'''      )
      parser.add_argument('''--mem_len'''			, type=_snake_case			, default=1600			, help='''length of the retained previous heads'''      )
      parser.add_argument('''--clamp_len'''			, type=_snake_case			, 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=_snake_case			, required=_snake_case			, 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=_snake_case			, default=''''''			, help='''Can be used for distant debugging.'''      )
      parser.add_argument('''--server_port'''			, type=_snake_case			, default=''''''			, help='''Can be used for distant debugging.'''      )
      lowerCAmelCase       :						str							= 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=_snake_case      )
            ptvsd.wait_for_attach()

      lowerCAmelCase       :						List[Any]							= torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''      )
      logger.info('''device: {}'''.format(_snake_case      )      )

      # 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       :						int							= TransfoXLCorpus.from_pretrained(args.model_name      )

      lowerCAmelCase       :						int							= corpus.get_iterator('''valid'''			, args.batch_size			, args.tgt_len			, device=_snake_case			, ext_len=args.ext_len      )
      lowerCAmelCase       :						Optional[Any]							= corpus.get_iterator('''test'''			, args.batch_size			, args.tgt_len			, device=_snake_case			, ext_len=args.ext_len      )

      # Load a pre-trained model
      lowerCAmelCase       :						Dict							= TransfoXLLMHeadModel.from_pretrained(args.model_name      )
      model.to(_snake_case      )

      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       :						Optional[Any]							= args.clamp_len
      if args.same_length:
            lowerCAmelCase       :						Optional[int]							= True

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

      # Run on test data.
      if args.split == "all":
            lowerCAmelCase       :						str							= evaluate(_snake_case      )
            lowerCAmelCase       :						Optional[Any]							= evaluate(_snake_case      )
      elif args.split == "valid":
            lowerCAmelCase       :						Optional[Any]							= evaluate(_snake_case      )
            lowerCAmelCase       :						List[str]							= None
      elif args.split == "test":
            lowerCAmelCase       :						List[str]							= evaluate(_snake_case      )
            lowerCAmelCase       :						Optional[int]							= None

      def format_log(_snake_case					:      Optional[int]			, _snake_case					:      Optional[int]      ):
            lowerCAmelCase       :						Any							= '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_snake_case			, _snake_case			, math.exp(_snake_case      )      )
            return log_str

      lowerCAmelCase       :						int							= ''''''
      if valid_loss is not None:
            log_str += format_log(_snake_case			, '''valid'''      )
      if test_loss is not None:
            log_str += format_log(_snake_case			, '''test'''      )

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


if __name__ == "__main__":
 main()






"""simple docstring"""





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


