"""simple docstring"""







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	:		str   =   logging.getLogger(__name__)



def       A__    (	)  ->							Optional[Any]:
   __lowerCamelCase    		=					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.'						)
   __lowerCamelCase    		=					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()

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

   __lowerCamelCase    		=					corpus.get_iterator('valid'      , args.batch_size      , args.tgt_len      , device=lowercase_      , ext_len=args.ext_len						)
   __lowerCamelCase    		=					corpus.get_iterator('test'      , args.batch_size      , args.tgt_len      , device=lowercase_      , ext_len=args.ext_len						)

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

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(lowercase_     :	Optional[int]						):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      __lowerCamelCase		,__lowerCamelCase    		=					0, 0.0
      __lowerCamelCase    		=					time.time()
      with torch.no_grad():
         __lowerCamelCase    		=					None
         for idx, (data, target, seq_len) in enumerate(lowercase_						):
            __lowerCamelCase    		=					model(lowercase_      , lm_labels=lowercase_      , mems=lowercase_						)
            __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					ret
            __lowerCamelCase    		=					loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         __lowerCamelCase    		=					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":
      __lowerCamelCase    		=					evaluate(lowercase_						)
      __lowerCamelCase    		=					evaluate(lowercase_						)
   elif args.split == "valid":
      __lowerCamelCase    		=					evaluate(lowercase_						)
      __lowerCamelCase    		=					None
   elif args.split == "test":
      __lowerCamelCase    		=					evaluate(lowercase_						)
      __lowerCamelCase    		=					None

   def format_log(lowercase_     :	Tuple      , lowercase_     :	Dict						):
      __lowerCamelCase    		=					'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowercase_      , lowercase_      , math.exp(lowercase_						)						)
      return log_str

   __lowerCamelCase    		=					''
   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()











"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
