
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
)
_lowercase											=			logging.getLogger(__name__)






def  _snake_case			(						):
	A  				=				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=128		, 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=1600		, help='length of the retained previous heads'       )
	parser.add_argument('--clamp_len'		, type=snake_case__		, 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=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.'       )
	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=snake_case__       )
		ptvsd.wait_for_attach()

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

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

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

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(snake_case__				:       int       ):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		A  				=				0, 0.0
		A  				=				time.time()
		with torch.no_grad():
			A  				=				None
			for idx, (data, target, seq_len) in enumerate(snake_case__       ):
				A  				=				model(snake_case__		, lm_labels=snake_case__		, mems=snake_case__       )
				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(snake_case__		, 1000 * total_time / (idx + 1)       )       )
		return total_loss / total_len

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

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

	A  				=				''
	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('=' * 100       )
	logger.info(snake_case__       )
	logger.info('=' * 100       )


if __name__ == "__main__":
		main()

"""simple docstring"""


from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      int =      '''prophetnet'''
						_lowerCamelCase:      Optional[Any] =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {
						    '''num_attention_heads''': '''num_encoder_attention_heads''',
						}


						def __init__(	self       : Tuple					,A_       : Optional[float] = 0.1					,A_       : Optional[Union[str, Callable]] = "gelu"					,A_       : Optional[int] = 3_0522					,A_       : Optional[int] = 1024					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[float] = 0.1					,A_       : Optional[float] = 0.1					,A_       : Optional[int] = 512					,A_       : Optional[float] = 0.02					,A_       : Optional[bool] = True					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 2					,A_       : Optional[int] = 32					,A_       : Optional[int] = 128					,A_       : Optional[bool] = False					,A_       : Optional[float] = 0.0					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 1					,A_       : Optional[int] = 2					,**A_       : Tuple					,)       ->				Any:
							A  				=				vocab_size
							A  				=				hidden_size
							A  				=				encoder_ffn_dim
							A  				=				num_encoder_layers
							A  				=				num_encoder_attention_heads
							A  				=				decoder_ffn_dim
							A  				=				num_decoder_layers
							A  				=				num_decoder_attention_heads
							A  				=				max_position_embeddings
							A  				=				init_std  # Normal(0, this parameter)
							A  				=				activation_function

							# parameters for prophetnet
							A  				=				ngram
							A  				=				num_buckets
							A  				=				relative_max_distance
							A  				=				disable_ngram_loss
							A  				=				eps

							# 3 Types of Dropout
							A  				=				attention_dropout
							A  				=				activation_dropout
							A  				=				dropout

							A  				=				use_cache

							super().__init__(
							    pad_token_id=A_					,bos_token_id=A_					,eos_token_id=A_					,is_encoder_decoder=A_					,add_cross_attention=A_					,decoder_start_token_id=A_					,**A_					,)


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							return self.num_encoder_layers + self.num_decoder_layers




						@num_hidden_layers.setter
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Any )       ->				int:
							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.' )