


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	:List[Any]  		=     logging.getLogger(__name__)



def 			_lowerCAmelCase						(							)->Optional[Any]:
   '''simple docstring'''






   snake_case_					    =					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=1_600		,						help="length of the retained previous heads"							)
   parser.add_argument("--clamp_len"		,						type=lowerCAmelCase_		,						default=1_000		,						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."							)
   snake_case_					    =					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()

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

   snake_case_					    =					corpus.get_iterator("valid"		,						args.batch_size		,						args.tgt_len		,						device=lowerCAmelCase_		,						ext_len=args.ext_len							)
   snake_case_					    =					corpus.get_iterator("test"		,						args.batch_size		,						args.tgt_len		,						device=lowerCAmelCase_		,						ext_len=args.ext_len							)

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

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(lowerCAmelCase_							:Any							):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      snake_case_    ,    snake_case_					    =					0, 0.0
      snake_case_					    =					time.time()
      with torch.no_grad():
         snake_case_					    =					None
         for idx, (data, target, seq_len) in enumerate(lowerCAmelCase_							):
            snake_case_					    =					model(lowerCAmelCase_		,						lm_labels=lowerCAmelCase_		,						mems=lowerCAmelCase_							)
            snake_case_    ,    snake_case_    ,    snake_case_					    =					ret
            snake_case_					    =					loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         snake_case_					    =					time.time() - start_time
      logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(lowerCAmelCase_		,						1_000 * total_time / (idx + 1)							)							)
      return total_loss / total_len

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

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

   snake_case_					    =					""
   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()








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'longt5'
  _SCREAMING_SNAKE_CASE		    =       ['past_key_values']
  _SCREAMING_SNAKE_CASE		    =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

     snake_case_					    =					self.feed_forward_proj.split("-"  )
     snake_case_					    =					act_info[-1]
     snake_case_					    =					act_info[0] == "gated"

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"  )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_					    =					"gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase    ,				eos_token_id=_lowerCAmelCase    ,				is_encoder_decoder=_lowerCAmelCase    ,				**_lowerCAmelCase    ,				)






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase    ,				direction="inputs"  )

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




