
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       =					logging.getLogger(__name__)



def 		lowercase_     (  )				->				Optional[Any]:
    """simple docstring"""

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

    # 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=UpperCAmelCase_					,						ext_len=args.ext_len						)
    snake_case						=   corpus.get_iterator("test"					,						args.batch_size					,						args.tgt_len					,						device=UpperCAmelCase_					,						ext_len=args.ext_len						)

    # Load a pre-trained model
    snake_case						=   TransfoXLLMHeadModel.from_pretrained(args.model_name						)
    model.to(UpperCAmelCase_						)

    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(A__						):
        # 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(UpperCAmelCase_						):
                snake_case						=   model(UpperCAmelCase_					,						lm_labels=UpperCAmelCase_					,						mems=UpperCAmelCase_						)
                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(UpperCAmelCase_					,						1000 * total_time / (idx + 1)						)						)
        return total_loss / total_len

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

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

    snake_case						=   ""
    if valid_loss is not None:
        log_str += format_log(UpperCAmelCase_					,						"valid"						)
    if test_loss is not None:
        log_str += format_log(UpperCAmelCase_					,						"test"						)

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


if __name__ == "__main__":
   main()




"""simple docstring"""



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    None

SCREAMING_SNAKE_CASE_       :						int					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Any					=    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_       :						Optional[int]					=    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       VOCAB_FILES_NAMES
    UpperCAmelCase    			=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase    			=       PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase    			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase    			=       DebertaVaTokenizer

    def __init__(  self:			Optional[int]     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			List[str]="[CLS]"     ,    UpperCamelCase:			Optional[Any]="[SEP]"     ,    UpperCamelCase:			int="[UNK]"     ,    UpperCamelCase:			List[Any]="[SEP]"     ,    UpperCamelCase:			Optional[Any]="[PAD]"     ,    UpperCamelCase:			Any="[CLS]"     ,    UpperCamelCase:			List[Any]="[MASK]"     ,    **UpperCamelCase:			List[str]     ,    ):






         """simple docstring"""


         super().__init__(
             UpperCamelCase     ,    tokenizer_file=UpperCamelCase     ,    do_lower_case=UpperCamelCase     ,    bos_token=UpperCamelCase     ,    eos_token=UpperCamelCase     ,    unk_token=UpperCamelCase     ,    sep_token=UpperCamelCase     ,    pad_token=UpperCamelCase     ,    cls_token=UpperCamelCase     ,    mask_token=UpperCamelCase     ,    split_by_punct=UpperCamelCase     ,    **UpperCamelCase     ,    )

         A__				      =	do_lower_case
         A__				      =	split_by_punct
         A__				      =	vocab_file
         A__				      =	False if not self.vocab_file else True

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			List[Any]     ,    UpperCamelCase:			Optional[int]=None							):






         """simple docstring"""


         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__				      =	[self.cls_token_id]
         A__				      =	[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Tuple=False							):






         """simple docstring"""


         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCamelCase     ,    token_ids_a=UpperCamelCase     ,    already_has_special_tokens=UpperCamelCase							)

         if token_ids_a is not None:
              return [1] + ([0] * len(UpperCamelCase							)) + [1] + ([0] * len(UpperCamelCase							)) + [1]
         return [1] + ([0] * len(UpperCamelCase							)) + [1]

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			Dict=None							):






         """simple docstring"""


         A__				      =	[self.sep_token_id]
         A__				      =	[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Optional[str] = None							):






         """simple docstring"""


         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer."""							)

         if not os.path.isdir(UpperCamelCase							):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
              return
         A__				      =	os.path.join(
             UpperCamelCase     ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase							):
              copyfile(self.vocab_file     ,    UpperCamelCase							)

         return (out_vocab_file,)






