



'''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
)
A_							:				int						=			logging.getLogger(__name__)




def 					snake_case_      (  )->	Dict:





       '''simple docstring'''


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

       _UpperCAmelCase						:		List[str]      =				torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
       logger.info("""device: {}""".format(lowerCAmelCase_	)	)

       # 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 )
       _UpperCAmelCase						:		Any      =				TransfoXLCorpus.from_pretrained(args.model_name	)

       _UpperCAmelCase						:		str      =				corpus.get_iterator("""valid""" ,     args.batch_size ,     args.tgt_len ,     device=lowerCAmelCase_ ,     ext_len=args.ext_len	)
       _UpperCAmelCase						:		Tuple      =				corpus.get_iterator("""test""" ,     args.batch_size ,     args.tgt_len ,     device=lowerCAmelCase_ ,     ext_len=args.ext_len	)

       # Load a pre-trained model
       _UpperCAmelCase						:		Optional[int]      =				TransfoXLLMHeadModel.from_pretrained(args.model_name	)
       model.to(lowerCAmelCase_	)

       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:
              _UpperCAmelCase						:		Union[str, Any]      =				args.clamp_len
       if args.same_length:
              _UpperCAmelCase						:		str      =				True

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

       # Run on test data.
       if args.split == "all":
              _UpperCAmelCase						:		Dict      =				evaluate(lowerCAmelCase_	)
              _UpperCAmelCase						:		Tuple      =				evaluate(lowerCAmelCase_	)
       elif args.split == "valid":
              _UpperCAmelCase						:		Union[str, Any]      =				evaluate(lowerCAmelCase_	)
              _UpperCAmelCase						:		Any      =				None
       elif args.split == "test":
              _UpperCAmelCase						:		Union[str, Any]      =				evaluate(lowerCAmelCase_	)
              _UpperCAmelCase						:		Optional[Any]      =				None

       def format_log(lowerCAmelCase_ ,     lowerCAmelCase_	):
              _UpperCAmelCase						:		int      =				"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(lowerCAmelCase_ ,     lowerCAmelCase_ ,     math.exp(lowerCAmelCase_	)	)
              return log_str

       _UpperCAmelCase						:		List[Any]      =				""""""
       if valid_loss is not None:
              log_str += format_log(lowerCAmelCase_ ,     """valid"""	)
       if test_loss is not None:
              log_str += format_log(lowerCAmelCase_ ,     """test"""	)

       logger.info("""=""" * 100	)
       logger.info(lowerCAmelCase_	)
       logger.info("""=""" * 100	)


if __name__ == "__main__":
   main()








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A_							:				Union[str, Any]						=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				Tuple						=			["""SegformerFeatureExtractor"""]
   A_							:				Tuple						=			["""SegformerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				Dict						=			[
       """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SegformerDecodeHead""",
       """SegformerForImageClassification""",
       """SegformerForSemanticSegmentation""",
       """SegformerLayer""",
       """SegformerModel""",
       """SegformerPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				List[str]						=			[
       """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSegformerDecodeHead""",
       """TFSegformerForImageClassification""",
       """TFSegformerForSemanticSegmentation""",
       """TFSegformerModel""",
       """TFSegformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_segformer import SegformerFeatureExtractor
      from .image_processing_segformer import SegformerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_segformer import (
          SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SegformerDecodeHead,
          SegformerForImageClassification,
          SegformerForSemanticSegmentation,
          SegformerLayer,
          SegformerModel,
          SegformerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_segformer import (
          TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSegformerDecodeHead,
          TFSegformerForImageClassification,
          TFSegformerForSemanticSegmentation,
          TFSegformerModel,
          TFSegformerPreTrainedModel,
      )

else:
   import sys

   A_							:				Optional[Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



