




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__a  :		Tuple			=    logging.get_logger(__name__)







@dataclass
class       UpperCAmelCase:



	"""simple docstring"""






	a	:					str
	a	:					str
	a	:					Optional[str]         =     None
	a	:					Optional[str]         =     None

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								return json.dumps(dataclasses.asdict(self				)       ,      indent=2				) + "\n"







@dataclass(frozen=A__     )
class       UpperCAmelCase:



	"""simple docstring"""






	a	:					List[int]
	a	:					Optional[List[int]]         =     None
	a	:					Optional[List[int]]         =     None
	a	:					Optional[Union[int, float]]         =     None

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								return json.dumps(dataclasses.asdict(self				)				) + "\n"







class       UpperCAmelCase:



	"""simple docstring"""






	def     __a	(     self       ,      lowerCamelCase				)					->			Union[str, Any]:





								"""simple docstring"""


								raise NotImplementedError()

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





								"""simple docstring"""


								raise NotImplementedError()

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





								"""simple docstring"""


								raise NotImplementedError()

	def     __a	(     self       ,      lowerCamelCase				)					->			Optional[int]:





								"""simple docstring"""


								raise NotImplementedError()

	def     __a	(     self				)					->			Union[str, Any]:





								"""simple docstring"""


								raise NotImplementedError()

	def     __a	(     self       ,      lowerCamelCase				)					->			Optional[Any]:





								"""simple docstring"""


								if len(self.get_labels()				) > 1:
															lowercase__  :       Any   					=							self.get_labels()[int(example.label				)]
								return example







	@classmethod
	def     __a	(     cls       ,      lowerCamelCase       ,      lowerCamelCase=None				)					->			Any:





								"""simple docstring"""


								with open(lowerCamelCase__       ,      "r"       ,      encoding="utf-8-sig"				) as f:
															return list(csv.reader(lowerCamelCase__       ,      delimiter="\t"       ,      quotechar=lowerCamelCase__				)				)







class       UpperCAmelCase(     A__     ):



	"""simple docstring"""






	def __init__(     self       ,      lowerCamelCase=None       ,      lowerCamelCase=None       ,      lowerCamelCase="classification"       ,      lowerCamelCase=False				)					->			Dict:





								"""simple docstring"""


								lowercase__  :       List[Any]   					=							[] if labels is None else labels
								lowercase__  :       Optional[int]   					=							[] if examples is None else examples
								lowercase__  :       List[Any]   					=							mode
								lowercase__  :       Optional[int]   					=							verbose

	def __len__(     self				)					->			Optional[Any]:





								"""simple docstring"""


								return len(self.examples				)

	def __getitem__(     self       ,      lowerCamelCase				)					->			Any:





								"""simple docstring"""


								if isinstance(lowerCamelCase__       ,      lowerCamelCase__				):
															return SingleSentenceClassificationProcessor(labels=self.labels       ,      examples=self.examples[idx]				)
								return self.examples[idx]

	@classmethod
	def     __a	(     cls       ,      lowerCamelCase       ,      lowerCamelCase=""       ,      lowerCamelCase=0       ,      lowerCamelCase=1       ,      lowerCamelCase=None       ,      lowerCamelCase=False       ,      **lowerCamelCase				)					->			Union[str, Any]:





								"""simple docstring"""


								lowercase__  :       List[str]   					=							cls(**lowerCamelCase__				)
								processor.add_examples_from_csv(
								    lowerCamelCase__       ,      split_name=lowerCamelCase__       ,      column_label=lowerCamelCase__       ,      column_text=lowerCamelCase__       ,      column_id=lowerCamelCase__       ,      skip_first_row=lowerCamelCase__       ,      overwrite_labels=lowerCamelCase__       ,      overwrite_examples=lowerCamelCase__       ,      )
								return processor

	@classmethod
	def     __a	(     cls       ,      lowerCamelCase       ,      lowerCamelCase=None       ,      **lowerCamelCase				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       Optional[int]   					=							cls(**lowerCamelCase__				)
								processor.add_examples(lowerCamelCase__       ,      labels=lowerCamelCase__				)
								return processor

	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase=""       ,      lowerCamelCase=0       ,      lowerCamelCase=1       ,      lowerCamelCase=None       ,      lowerCamelCase=False       ,      lowerCamelCase=False       ,      lowerCamelCase=False       ,      )					->			Optional[Any]:





								"""simple docstring"""


								lowercase__  :       Optional[Any]   					=							self._read_tsv(lowerCamelCase__				)
								if skip_first_row:
															lowercase__  :       Dict   					=							lines[1:]
								lowercase__  :       Optional[int]   					=							[]
								lowercase__  :       Tuple   					=							[]
								lowercase__  :       Dict   					=							[]
								for i, line in enumerate(lowerCamelCase__				):
															texts.append(line[column_text]				)
															labels.append(line[column_label]				)
															if column_id is not None:
																						ids.append(line[column_id]				)
															else:
																						lowercase__  :       Any   					=							f"""{split_name}-{i}""" if split_name else str(lowerCamelCase__				)
																						ids.append(lowerCamelCase__				)

								return self.add_examples(
								    lowerCamelCase__       ,      lowerCamelCase__       ,      lowerCamelCase__       ,      overwrite_labels=lowerCamelCase__       ,      overwrite_examples=lowerCamelCase__				)

	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase=None       ,      lowerCamelCase=None       ,      lowerCamelCase=False       ,      lowerCamelCase=False				)					->			Union[str, Any]:





								"""simple docstring"""


								if labels is not None and len(lowerCamelCase__				) != len(lowerCamelCase__				):
															raise ValueError(
															    f"""Text and labels have mismatched lengths {len(lowerCamelCase__				)} and {len(lowerCamelCase__				)}"""				)
								if ids is not None and len(lowerCamelCase__				) != len(lowerCamelCase__				):
															raise ValueError(f"""Text and ids have mismatched lengths {len(lowerCamelCase__				)} and {len(lowerCamelCase__				)}"""				)
								if ids is None:
															lowercase__  :       Union[str, Any]   					=							[None] * len(lowerCamelCase__				)
								if labels is None:
															lowercase__  :       List[str]   					=							[None] * len(lowerCamelCase__				)
								lowercase__  :       Any   					=							[]
								lowercase__  :       Dict   					=							set()
								for text_or_text_and_label, label, guid in zip(lowerCamelCase__       ,      lowerCamelCase__       ,      lowerCamelCase__				):
															if isinstance(lowerCamelCase__       ,      (tuple, list)				) and label is None:
																						lowercase__					,   lowercase__  :       Optional[int]   					=							text_or_text_and_label
															else:
																						lowercase__  :       List[str]   					=							text_or_text_and_label
															added_labels.add(lowerCamelCase__				)
															examples.append(InputExample(guid=lowerCamelCase__       ,      text_a=lowerCamelCase__       ,      text_b=lowerCamelCase__       ,      label=lowerCamelCase__				)				)

								# Update examples
								if overwrite_examples:
															lowercase__  :       Optional[Any]   					=							examples
								else:
															self.examples.extend(lowerCamelCase__				)

								# Update labels
								if overwrite_labels:
															lowercase__  :       Tuple   					=							list(lowerCamelCase__				)
								else:
															lowercase__  :       Tuple   					=							list(set(self.labels				).union(lowerCamelCase__				)				)

								return self.examples







	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase=None       ,      lowerCamelCase=False       ,      lowerCamelCase=0       ,      lowerCamelCase=True       ,      lowerCamelCase=None       ,      )					->			Union[str, Any]:





								"""simple docstring"""


								if max_length is None:
															lowercase__  :       List[str]   					=							tokenizer.max_len

								lowercase__  :       Union[str, Any]   					=							{label: i for i, label in enumerate(self.labels				)}

								lowercase__  :       Any   					=							[]
								for ex_index, example in enumerate(self.examples				):
															if ex_index % 10000 == 0:
																						logger.info(f"""Tokenizing example {ex_index}"""				)

															lowercase__  :       Dict   					=							tokenizer.encode(
															    example.text_a       ,      add_special_tokens=lowerCamelCase__       ,      max_length=min(lowerCamelCase__       ,      tokenizer.max_len				)       ,      )
															all_input_ids.append(lowerCamelCase__				)

								lowercase__  :       Tuple   					=							max(len(lowerCamelCase__				) for input_ids in all_input_ids				)

								lowercase__  :       str   					=							[]
								for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase__       ,      self.examples				)				):
															if ex_index % 10000 == 0:
																						logger.info(f"""Writing example {ex_index}/{len(self.examples				)}"""				)
															# The mask has 1 for real tokens and 0 for padding tokens. Only real
															# tokens are attended to.
															lowercase__  :       str   					=							[1 if mask_padding_with_zero else 0] * len(lowerCamelCase__				)

															# Zero-pad up to the sequence length.
															lowercase__  :       Union[str, Any]   					=							batch_length - len(lowerCamelCase__				)
															if pad_on_left:
																						lowercase__  :       str   					=							([pad_token] * padding_length) + input_ids
																						lowercase__  :       Optional[int]   					=							([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
															else:
																						lowercase__  :       List[Any]   					=							input_ids + ([pad_token] * padding_length)
																						lowercase__  :       Dict   					=							attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

															if len(lowerCamelCase__				) != batch_length:
																						raise ValueError(f"""Error with input length {len(lowerCamelCase__				)} vs {batch_length}"""				)
															if len(lowerCamelCase__				) != batch_length:
																						raise ValueError(f"""Error with input length {len(lowerCamelCase__				)} vs {batch_length}"""				)

															if self.mode == "classification":
																						lowercase__  :       Dict   					=							label_map[example.label]
															elif self.mode == "regression":
																						lowercase__  :       List[Any]   					=							float(example.label				)
															else:
																						raise ValueError(self.mode				)

															if ex_index < 5 and self.verbose:
																						logger.info("*** Example ***"				)
																						logger.info(f"""guid: {example.guid}"""				)
																						logger.info(f"""input_ids: {" ".join([str(lowerCamelCase__				) for x in input_ids]				)}"""				)
																						logger.info(f"""attention_mask: {" ".join([str(lowerCamelCase__				) for x in attention_mask]				)}"""				)
																						logger.info(f"""label: {example.label} (id = {label})"""				)

															features.append(InputFeatures(input_ids=lowerCamelCase__       ,      attention_mask=lowerCamelCase__       ,      label=lowerCamelCase__				)				)

								if return_tensors is None:
															return features
								elif return_tensors == "tf":
															if not is_tf_available():
																						raise RuntimeError("return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported"				)
															import tensorflow as tf

															def gen():
																						for ex in features:
																													yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

															lowercase__  :       int   					=							tf.data.Dataset.from_generator(
															    lowerCamelCase__       ,      ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)       ,      ({"input_ids": tf.TensorShape([None]				), "attention_mask": tf.TensorShape([None]				)}, tf.TensorShape([]				))       ,      )
															return dataset
								elif return_tensors == "pt":
															if not is_torch_available():
																						raise RuntimeError("return_tensors set to \'pt\' but PyTorch can\'t be imported"				)
															import torch
															from torch.utils.data import TensorDataset

															lowercase__  :       Any   					=							torch.tensor([f.input_ids for f in features]       ,      dtype=torch.long				)
															lowercase__  :       str   					=							torch.tensor([f.attention_mask for f in features]       ,      dtype=torch.long				)
															if self.mode == "classification":
																						lowercase__  :       Any   					=							torch.tensor([f.label for f in features]       ,      dtype=torch.long				)
															elif self.mode == "regression":
																						lowercase__  :       str   					=							torch.tensor([f.label for f in features]       ,      dtype=torch.float				)

															lowercase__  :       Optional[int]   					=							TensorDataset(lowerCamelCase__       ,      lowerCamelCase__       ,      lowerCamelCase__				)
															return dataset
								else:
															raise ValueError("return_tensors should be one of \'tf\' or \'pt\'"				)




"""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     :  Dict										=					logging.getLogger(__name__)



def 				lowerCAmelCase_(      )		->				Optional[int]:
				_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=1_28  ,      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=16_00  ,      help='''length of the retained previous heads'''							)
				parser.add_argument('''--clamp_len'''  ,      type=lowercase_  ,      default=10_00  ,      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_   :							Any							):
								# 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_  ,      10_00 * 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_   :							Optional[Any]  ,      lowercase_   :							Optional[Any]							):
								_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('''=''' * 1_00							)
				logger.info(lowercase_							)
				logger.info('''=''' * 1_00							)


if __name__ == "__main__":
			main()
