"""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
)
lowerCAmelCase_:       Optional[int]			     =       logging.getLogger(__name__)







def        __a (		):






     '''simple docstring'''



     lowercase__								=				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=10     ,					help="batch size"   )
     parser.add_argument("--tgt_len"     ,					type=A     ,					default=1_28     ,					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=16_00     ,					help="length of the retained previous heads"   )
     parser.add_argument("--clamp_len"     ,					type=A     ,					default=10_00     ,					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."   )
     lowercase__								=				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()

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

     lowercase__								=				corpus.get_iterator("valid"     ,					args.batch_size     ,					args.tgt_len     ,					device=A     ,					ext_len=args.ext_len   )
     lowercase__								=				corpus.get_iterator("test"     ,					args.batch_size     ,					args.tgt_len     ,					device=A     ,					ext_len=args.ext_len   )

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

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(A   ):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          lowercase__			,    lowercase__								=				0, 0.0
          lowercase__								=				time.time()
          with torch.no_grad():
               lowercase__								=				None
               for idx, (data, target, seq_len) in enumerate(A   ):
                    lowercase__								=				model(A     ,					lm_labels=A     ,					mems=A   )
                    lowercase__			,    lowercase__			,    lowercase__								=				ret
                    lowercase__								=				loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               lowercase__								=				time.time() - start_time
          logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(A     ,					10_00 * total_time / (idx + 1)   )   )
          return total_loss / total_len

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

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

     lowercase__								=				""
     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_00   )
     logger.info(A   )
     logger.info("=" * 1_00   )


if __name__ == "__main__":
 main()





"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




