




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
)
_lowerCamelCase					:     Any					  =  logging.getLogger(__name__)




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

				_lowerCAmelCase			:			int        =					torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""				)
				logger.info("""device: {}""".format(UpperCamelCase_				)				)

				# 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			:			str        =					TransfoXLCorpus.from_pretrained(args.model_name				)

				_lowerCAmelCase			:			List[str]        =					corpus.get_iterator("""valid"""					,			args.batch_size					,			args.tgt_len					,			device=UpperCamelCase_					,			ext_len=args.ext_len				)
				_lowerCAmelCase			:			int        =					corpus.get_iterator("""test"""					,			args.batch_size					,			args.tgt_len					,			device=UpperCamelCase_					,			ext_len=args.ext_len				)

				# Load a pre-trained model
				_lowerCAmelCase			:			List[str]        =					TransfoXLLMHeadModel.from_pretrained(args.model_name				)
				model.to(UpperCamelCase_				)

				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			:			Any        =					args.clamp_len
				if args.same_length:
								_lowerCAmelCase			:			int        =					True

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

				# Run on test data.
				if args.split == "all":
								_lowerCAmelCase			:			List[Any]        =					evaluate(UpperCamelCase_				)
								_lowerCAmelCase			:			Tuple        =					evaluate(UpperCamelCase_				)
				elif args.split == "valid":
								_lowerCAmelCase			:			Tuple        =					evaluate(UpperCamelCase_				)
								_lowerCAmelCase			:			Optional[int]        =					None
				elif args.split == "test":
								_lowerCAmelCase			:			Any        =					evaluate(UpperCamelCase_				)
								_lowerCAmelCase			:			List[Any]        =					None

				def format_log(UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       Dict				):
								_lowerCAmelCase			:			Dict        =					"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(UpperCamelCase_					,			UpperCamelCase_					,			math.exp(UpperCamelCase_				)				)
								return log_str

				_lowerCAmelCase			:			int        =					""""""
				if valid_loss is not None:
								log_str += format_log(UpperCamelCase_					,			"""valid"""				)
				if test_loss is not None:
								log_str += format_log(UpperCamelCase_					,			"""test"""				)

				logger.info("""=""" * 100				)
				logger.info(UpperCamelCase_				)
				logger.info("""=""" * 100				)


if __name__ == "__main__":
	main()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

