

'''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_    =   logging.getLogger(__name__)

def   lowerCamelCase__       (      )		->    str:



    """simple docstring"""







    _snake_case : int												=		argparse.ArgumentParser(description='PyTorch Transformer Language Model')
    parser.add_argument('--model_name'			,      type=a__			,      default='transfo-xl-wt103'			,      help='pretrained model name')
    parser.add_argument(
        '--split'			,      type=a__			,      default='test'			,      choices=['all', 'valid', 'test']			,      help='which split to evaluate')
    parser.add_argument('--batch_size'			,      type=a__			,      default=1_0			,      help='batch size')
    parser.add_argument('--tgt_len'			,      type=a__			,      default=1_2_8			,      help='number of tokens to predict')
    parser.add_argument('--ext_len'			,      type=a__			,      default=0			,      help='length of the extended context')
    parser.add_argument('--mem_len'			,      type=a__			,      default=1_6_0_0			,      help='length of the retained previous heads')
    parser.add_argument('--clamp_len'			,      type=a__			,      default=1_0_0_0			,      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=a__			,      required=a__			,      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=a__			,      default=''			,      help='Can be used for distant debugging.')
    parser.add_argument('--server_port'			,      type=a__			,      default=''			,      help='Can be used for distant debugging.')
    _snake_case : int												=		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=a__)
        ptvsd.wait_for_attach()

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

    # 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 )
    _snake_case : List[Any]												=		TransfoXLCorpus.from_pretrained(args.model_name)

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

    # Load a pre-trained model
    _snake_case : List[Any]												=		TransfoXLLMHeadModel.from_pretrained(args.model_name)
    model.to(a__)

    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:
        _snake_case : List[Any]												=		args.clamp_len
    if args.same_length:
        _snake_case : int												=		True

    ###############################################################################
    # Evaluation code
    ###############################################################################
    def evaluate(a__):
        # Turn on evaluation mode which disables dropout.
        model.eval()
        _snake_case					,							_snake_case : Tuple												=		0, 0.0
        _snake_case : Dict												=		time.time()
        with torch.no_grad():
            _snake_case : Union[str, Any]												=		None
            for idx, (data, target, seq_len) in enumerate(a__):
                _snake_case : Optional[int]												=		model(a__			,      lm_labels=a__			,      mems=a__)
                _snake_case					,							_snake_case					,							_snake_case : Dict												=		ret
                _snake_case : int												=		loss.mean()
                total_loss += seq_len * loss.item()
                total_len += seq_len
            _snake_case : Optional[int]												=		time.time() - start_time
        logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(a__			,      1_0_0_0 * total_time / (idx + 1)))
        return total_loss / total_len

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

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

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

    logger.info('=' * 1_0_0)
    logger.info(a__)
    logger.info('=' * 1_0_0)


if __name__ == "__main__":
   main()






'''simple docstring'''



import copy

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


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				'''fsmt_decoder'''




      def __init__(					self :		Any     ,					snake_case :		int=0     ,					snake_case :		Dict=0   ):



          """simple docstring"""





          super().__init__()
          _snake_case : int												=		vocab_size
          _snake_case : Dict												=		bos_token_id



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''fsmt'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Tuple=["en", "de"]     ,					snake_case :		Optional[int]=42024     ,					snake_case :		List[str]=42024     ,					snake_case :		Union[str, Any]="relu"     ,					snake_case :		Optional[Any]=1024     ,					snake_case :		Union[str, Any]=200     ,					snake_case :		int=1024     ,					snake_case :		str=4096     ,					snake_case :		Dict=12     ,					snake_case :		str=16     ,					snake_case :		List[str]=0.0     ,					snake_case :		List[Any]=4096     ,					snake_case :		str=12     ,					snake_case :		str=16     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		str=0.1     ,					snake_case :		Tuple=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		List[str]=2     ,					snake_case :		int=True     ,					snake_case :		Any=True     ,					snake_case :		str=False     ,					snake_case :		Union[str, Any]=5     ,					snake_case :		Dict=1.0     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Optional[int]=1     ,					snake_case :		int=0     ,					snake_case :		Optional[int]=2     ,					snake_case :		Optional[Any]=2     ,					**snake_case :		Any     ,					):



          """simple docstring"""





          _snake_case : int												=		langs
          _snake_case : str												=		src_vocab_size
          _snake_case : List[Any]												=		tgt_vocab_size
          _snake_case : Optional[int]												=		d_model  # encoder_embed_dim and decoder_embed_dim

          _snake_case : Optional[Any]												=		encoder_ffn_dim
          _snake_case : Dict												=		encoder_layers
          _snake_case : List[Any]												=		encoder_attention_heads
          _snake_case : Any												=		encoder_layerdrop
          _snake_case : List[str]												=		decoder_layerdrop
          _snake_case : str												=		decoder_ffn_dim
          _snake_case : Union[str, Any]												=		decoder_layers
          _snake_case : Tuple												=		decoder_attention_heads
          _snake_case : str												=		max_position_embeddings
          _snake_case : Dict												=		init_std  # Normal(0, this parameter)
          _snake_case : List[str]												=		activation_function

          _snake_case : Any												=		DecoderConfig(vocab_size=snake_case     ,					bos_token_id=snake_case   )
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          _snake_case : str												=		scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _snake_case : int												=		attention_dropout
          _snake_case : List[Any]												=		activation_dropout
          _snake_case : Optional[int]												=		dropout

          _snake_case : Union[str, Any]												=		use_cache
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					decoder_start_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					tie_word_embeddings=snake_case     ,					forced_eos_token_id=snake_case     ,					max_length=snake_case     ,					num_beams=snake_case     ,					length_penalty=snake_case     ,					early_stopping=snake_case     ,					**snake_case     ,					)




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Any												=		copy.deepcopy(self.__dict__   )
          _snake_case : str												=		self.decoder.to_dict()
          _snake_case : Dict												=		self.__class__.model_type
          return output



