
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							:List[Any]											=       logging.getLogger(__name__)


def 		UpperCAmelCase    (			)	->			Dict:







				"""simple docstring"""



				__A         =				argparse.ArgumentParser(description="PyTorch Transformer Language Model"		)
				parser.add_argument("--model_name"	,     type=a_	,     default="transfo-xl-wt103"	,     help="pretrained model name"		)
				parser.add_argument(
				    "--split"	,     type=a_	,     default="test"	,     choices=["all", "valid", "test"]	,     help="which split to evaluate"		)
				parser.add_argument("--batch_size"	,     type=a_	,     default=1_0	,     help="batch size"		)
				parser.add_argument("--tgt_len"	,     type=a_	,     default=1_2_8	,     help="number of tokens to predict"		)
				parser.add_argument("--ext_len"	,     type=a_	,     default=0	,     help="length of the extended context"		)
				parser.add_argument("--mem_len"	,     type=a_	,     default=1_6_0_0	,     help="length of the retained previous heads"		)
				parser.add_argument("--clamp_len"	,     type=a_	,     default=1_0_0_0	,     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=a_	,     required=a_	,     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=a_	,     default=""	,     help="Can be used for distant debugging."		)
				parser.add_argument("--server_port"	,     type=a_	,     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=a_		)
								ptvsd.wait_for_attach()

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

				# 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=a_	,     ext_len=args.ext_len		)
				__A         =				corpus.get_iterator("test"	,     args.batch_size	,     args.tgt_len	,     device=a_	,     ext_len=args.ext_len		)

				# Load a pre-trained model
				__A         =				TransfoXLLMHeadModel.from_pretrained(args.model_name		)
				model.to(a_		)

				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(a_		):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								__A    ,							__A         =				0, 0.0
								__A         =				time.time()
								with torch.no_grad():
												__A         =				None
												for idx, (data, target, seq_len) in enumerate(a_		):
																__A         =				model(a_	,     lm_labels=a_	,     mems=a_		)
																__A    ,							__A    ,							__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(a_	,     1_0_0_0 * total_time / (idx + 1)		)		)
								return total_loss / total_len

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

				def format_log(a_	,     a_		):
								__A         =				"| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(a_	,     a_	,     math.exp(a_		)		)
								return log_str

				__A         =				""
				if valid_loss is not None:
								log_str += format_log(a_	,     "valid"		)
				if test_loss is not None:
								log_str += format_log(a_	,     "test"		)

				logger.info("=" * 1_0_0		)
				logger.info(a_		)
				logger.info("=" * 1_0_0		)


if __name__ == "__main__":
					main()


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
