
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        lowerCamelCase__    (    ):




			"""simple docstring"""


			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(__lowerCAmelCase  :  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(__lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  str    ):
						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()





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



