

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
)
_UpperCamelCase			       =			logging.getLogger(__name__)
def 						UpperCamelCase_(			)       -> Any:
 UpperCAmelCase__							=				argparse.ArgumentParser(description='PyTorch Transformer Language Model'   )
 parser.add_argument('--model_name'  ,    type=snake_case__  ,    default='transfo-xl-wt103'  ,    help='pretrained model name'   )
 parser.add_argument(
     '--split'  ,    type=snake_case__  ,    default='test'  ,    choices=['all', 'valid', 'test']  ,    help='which split to evaluate'   )
 parser.add_argument('--batch_size'  ,    type=snake_case__  ,    default=10  ,    help='batch size'   )
 parser.add_argument('--tgt_len'  ,    type=snake_case__  ,    default=1_28  ,    help='number of tokens to predict'   )
 parser.add_argument('--ext_len'  ,    type=snake_case__  ,    default=0  ,    help='length of the extended context'   )
 parser.add_argument('--mem_len'  ,    type=snake_case__  ,    default=16_00  ,    help='length of the retained previous heads'   )
 parser.add_argument('--clamp_len'  ,    type=snake_case__  ,    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=snake_case__  ,    required=snake_case__  ,    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=snake_case__  ,    default=''  ,    help='Can be used for distant debugging.'   )
 parser.add_argument('--server_port'  ,    type=snake_case__  ,    default=''  ,    help='Can be used for distant debugging.'   )
 UpperCAmelCase__							=				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=snake_case__   )
  ptvsd.wait_for_attach()

 UpperCAmelCase__							=				torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'   )
 logger.info('device: {}'.format(snake_case__   )   )

 # 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 )
 UpperCAmelCase__							=				TransfoXLCorpus.from_pretrained(args.model_name   )

 UpperCAmelCase__							=				corpus.get_iterator('valid'  ,    args.batch_size  ,    args.tgt_len  ,    device=snake_case__  ,    ext_len=args.ext_len   )
 UpperCAmelCase__							=				corpus.get_iterator('test'  ,    args.batch_size  ,    args.tgt_len  ,    device=snake_case__  ,    ext_len=args.ext_len   )

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

 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:
  UpperCAmelCase__							=				args.clamp_len
 if args.same_length:
  UpperCAmelCase__							=				True

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

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

 def format_log(snake_case__:  Tuple  ,    snake_case__:  Union[str, Any]   ):
  UpperCAmelCase__							=				'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(snake_case__  ,    snake_case__  ,    math.exp(snake_case__   )   )
  return log_str

 UpperCAmelCase__							=				''
 if valid_loss is not None:
  log_str += format_log(snake_case__  ,    'valid'   )
 if test_loss is not None:
  log_str += format_log(snake_case__  ,    'test'   )

 logger.info('=' * 1_00   )
 logger.info(snake_case__   )
 logger.info('=' * 1_00   )


if __name__ == "__main__":
 main()









'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

