






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 :    Tuple								=				logging.getLogger(__name__)



def      _SCREAMING_SNAKE_CASE  (	)  -> List[str]:





    """simple docstring"""

    __A             =		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=1_0     ,			help="""batch size"""      )
    parser.add_argument("""--tgt_len"""     ,			type=__lowercase     ,			default=1_2_8     ,			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=1_6_0_0     ,			help="""length of the retained previous heads"""      )
    parser.add_argument("""--clamp_len"""     ,			type=__lowercase     ,			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=__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."""      )
    __A             =		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()

    __A             =		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 )
    __A             =		TransfoXLCorpus.from_pretrained(args.model_name      )

    __A             =		corpus.get_iterator("""valid"""     ,			args.batch_size     ,			args.tgt_len     ,			device=__lowercase     ,			ext_len=args.ext_len      )
    __A             =		corpus.get_iterator("""test"""     ,			args.batch_size     ,			args.tgt_len     ,			device=__lowercase     ,			ext_len=args.ext_len      )

    # Load a pre-trained model
    __A             =		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:
        __A             =		args.clamp_len
    if args.same_length:
        __A             =		True

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

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

    def format_log(__lowercase				:     Tuple     ,			__lowercase				:     str      ):
        __A             =		"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(__lowercase     ,			__lowercase     ,			math.exp(__lowercase      )      )
        return log_str

    __A             =		""""""
    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("""=""" * 1_0_0      )
    logger.info(__lowercase      )
    logger.info("""=""" * 1_0_0      )


if __name__ == "__main__":
  main()












import copy

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


__a :    Tuple								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"hybrid-clip"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     Optional[Any]=512	,		**UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)

         if "text_config" not in kwargs:
             raise ValueError("""`text_config` can not be `None`."""			)

         if "vision_config" not in kwargs:
             raise ValueError("""`vision_config` can not be `None`."""			)

         __A             =		kwargs.pop("""text_config"""			)
         __A             =		kwargs.pop("""vision_config"""			)

         __A             =		text_config.pop("""model_type"""			)
         __A             =		vision_config.pop("""model_type"""			)

         from transformers import AutoConfig

         __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			)

         if vision_model_type == "clip":
             __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			).vision_config
         elif vision_model_type == "clip_vision_model":
             from transformers import CLIPVisionConfig

             __A             =		CLIPVisionConfig(**UpperCamelCase_			)
         else:
             __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			)

         __A             =		projection_dim
         __A             =		1.0




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     PretrainedConfig	,		UpperCamelCase_    :     PretrainedConfig	,		**UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         return cls(text_config=text_config.to_dict()	,		vision_config=vision_config.to_dict()	,		**UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)
         __A             =		self.text_config.to_dict()
         __A             =		self.vision_config.to_dict()
         __A             =		self.__class__.model_type
         return output




