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

       _UpperCamelCase  :							Optional[Any]	    =	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
       logger.info('device: {}'.format(lowercase_					)					)

       # 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  :							List[str]	    =	TransfoXLCorpus.from_pretrained(args.model_name					)

       _UpperCamelCase  :							Union[str, Any]	    =	corpus.get_iterator('valid'		,    args.batch_size		,    args.tgt_len		,    device=lowercase_		,    ext_len=args.ext_len					)
       _UpperCamelCase  :							Dict	    =	corpus.get_iterator('test'		,    args.batch_size		,    args.tgt_len		,    device=lowercase_		,    ext_len=args.ext_len					)

       # Load a pre-trained model
       _UpperCamelCase  :							Optional[Any]	    =	TransfoXLLMHeadModel.from_pretrained(args.model_name					)
       model.to(lowercase_					)

       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  :							int	    =	args.clamp_len
       if args.same_length:
              _UpperCamelCase  :							List[str]	    =	True

       ###############################################################################
       # Evaluation code
       ###############################################################################
       def evaluate(lowercase_:						Tuple					):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	0, 0.0
              _UpperCamelCase  :							Any	    =	time.time()
              with torch.no_grad():
                     _UpperCamelCase  :							Optional[int]	    =	None
                     for idx, (data, target, seq_len) in enumerate(lowercase_					):
                            _UpperCamelCase  :							List[str]	    =	model(lowercase_		,    lm_labels=lowercase_		,    mems=lowercase_					)
                            _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	ret
                            _UpperCamelCase  :							Optional[int]	    =	loss.mean()
                            total_loss += seq_len * loss.item()
                            total_len += seq_len
                     _UpperCamelCase  :							Dict	    =	time.time() - start_time
              logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(lowercase_		,    1000 * total_time / (idx + 1)					)					)
              return total_loss / total_len

       # Run on test data.
       if args.split == "all":
              _UpperCamelCase  :							str	    =	evaluate(lowercase_					)
              _UpperCamelCase  :							Tuple	    =	evaluate(lowercase_					)
       elif args.split == "valid":
              _UpperCamelCase  :							str	    =	evaluate(lowercase_					)
              _UpperCamelCase  :							str	    =	None
       elif args.split == "test":
              _UpperCamelCase  :							int	    =	evaluate(lowercase_					)
              _UpperCamelCase  :							Dict	    =	None

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

       _UpperCamelCase  :							Optional[Any]	    =	''
       if valid_loss is not None:
              log_str += format_log(lowercase_		,    'valid'					)
       if test_loss is not None:
              log_str += format_log(lowercase_		,    'test'					)

       logger.info('=' * 100					)
       logger.info(lowercase_					)
       logger.info('=' * 100					)


if __name__ == "__main__":
 main()




import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput




@dataclass
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]







class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		int	)			->				Tuple:
        _UpperCamelCase  :							str	    =	CustomOutput(images=np.random.rand(1							,      3							,      4							,      4	)	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							str	    =	CustomOutput(images=[PIL.Image.new('RGB'							,      (4, 4)	)]	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        # test output reinitialization with a `dict` for compatibility with `accelerate`
        _UpperCamelCase  :							Tuple	    =	CustomOutput({'images': np.random.rand(1							,      3							,      4							,      4	)}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							int	    =	CustomOutput({'images': [PIL.Image.new('RGB'							,      (4, 4)	)]}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)



