
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 		UpperCAmelCase    (			)	->			Dict:







    """simple docstring"""



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

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

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

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

    ###############################################################################
    # Evaluation code
    ###############################################################################
    def evaluate(a_		):
        # 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(a_		):
                __A         =				model(a_	,     lm_labels=a_	,     mems=a_		)
                __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(a_	,     1_0_0_0 * total_time / (idx + 1)		)		)
        return total_loss / total_len

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

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

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


if __name__ == "__main__":
     main()


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
