




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCamelCase					:     Dict					  =  logging.getLogger(__name__)
@dataclass
class 				__snake_case     :
				lowerCAmelCase__			=   field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
				lowerCAmelCase__			=   field(
				    default=_a  ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
				lowerCAmelCase__			=   field(
				    default=_a  ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
				lowerCAmelCase__			=   field(
				    default=_a  ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,		)






@dataclass
class 				__snake_case     :
				lowerCAmelCase__			=   field(
				    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()						)}						)
				lowerCAmelCase__			=   field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
				lowerCAmelCase__			=   field(
				    default=1_2_8  ,		metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }  ,		)
				lowerCAmelCase__			=   field(
				    default=_a  ,		metadata={"help": "Overwrite the cached training and evaluation sets"}						)






def 							_UpperCAmelCase     (UpperCamelCase_       :       List[InputFeatures]				):
				'''simple docstring'''
				_lowerCAmelCase			:			str        =					default_data_collator(UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					batch.pop("""pairID"""					,			UpperCamelCase_				)
				return batch




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_lowerCAmelCase			:			Any        =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
				_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir				)
				    and os.listdir(training_args.output_dir				)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""				)

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,			datefmt="""%m/%d/%Y %H:%M:%S"""					,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,			)
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""					,			training_args.local_rank					,			training_args.device					,			training_args.n_gpu					,			bool(training_args.local_rank != -1				)					,			training_args.fpaa					,			)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank				):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""					,			UpperCamelCase_				)

				# Set seed
				set_seed(training_args.seed				)

				try:
								_lowerCAmelCase			:			List[Any]        =					hans_tasks_num_labels[data_args.task_name]
				except KeyError:
								raise ValueError("""Task not found: %s""" % (data_args.task_name)				)

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				_lowerCAmelCase			:			Union[str, Any]        =					AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,			num_labels=UpperCamelCase_					,			finetuning_task=data_args.task_name					,			cache_dir=model_args.cache_dir					,			)
				_lowerCAmelCase			:			Union[str, Any]        =					AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,			cache_dir=model_args.cache_dir					,			)
				_lowerCAmelCase			:			Any        =					AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path					,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)					,			config=UpperCamelCase_					,			cache_dir=model_args.cache_dir					,			)

				# Get datasets
				_lowerCAmelCase			:			Optional[Any]        =					(
				    HansDataset(
				        data_dir=data_args.data_dir					,			tokenizer=UpperCamelCase_					,			task=data_args.task_name					,			max_seq_length=data_args.max_seq_length					,			overwrite_cache=data_args.overwrite_cache					,			)
				    if training_args.do_train
				    else None
				)
				_lowerCAmelCase			:			Tuple        =					(
				    HansDataset(
				        data_dir=data_args.data_dir					,			tokenizer=UpperCamelCase_					,			task=data_args.task_name					,			max_seq_length=data_args.max_seq_length					,			overwrite_cache=data_args.overwrite_cache					,			evaluate=UpperCamelCase_					,			)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				_lowerCAmelCase			:			List[Any]        =					Trainer(
				    model=UpperCamelCase_					,			args=UpperCamelCase_					,			train_dataset=UpperCamelCase_					,			eval_dataset=UpperCamelCase_					,			data_collator=UpperCamelCase_					,			)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""				)

								_lowerCAmelCase			:			Dict        =					trainer.predict(UpperCamelCase_				)
								_lowerCAmelCase			:			str        =					output.predictions
								_lowerCAmelCase			:			List[str]        =					np.argmax(UpperCamelCase_					,			axis=1				)

								_lowerCAmelCase			:			str        =					[ex.pairID for ex in eval_dataset]
								_lowerCAmelCase			:			Tuple        =					os.path.join(training_args.output_dir					,			"""hans_predictions.txt"""				)
								_lowerCAmelCase			:			Dict        =					eval_dataset.get_labels()
								if trainer.is_world_master():
												with open(UpperCamelCase_					,			"""w"""				) as writer:
																writer.write("""pairID,gold_label\n"""				)
																for pid, pred in zip(UpperCamelCase_					,			UpperCamelCase_				):
																				writer.write("""ex""" + str(UpperCamelCase_				) + """,""" + label_list[int(UpperCamelCase_				)] + """\n"""				)

								trainer._log(output.metrics				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
	main()







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


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

_lowerCamelCase					:     str					  =  {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "reformer"
				lowerCAmelCase__			=   ["past_buckets_states"]
				lowerCAmelCase__			=   {}



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str=64						,		_UpperCAmelCase					:						Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,		_UpperCAmelCase					:						Optional[int]=1.0						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						List[Any]=[64, 64]						,		_UpperCAmelCase					:						str=[64, 192]						,		_UpperCAmelCase					:						List[Any]=0						,		_UpperCAmelCase					:						Optional[Any]=2						,		_UpperCAmelCase					:						Dict=512						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Tuple="relu"						,		_UpperCAmelCase					:						Tuple=0.05						,		_UpperCAmelCase					:						int=256						,		_UpperCAmelCase					:						List[str]=0.02						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=0.05						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=64						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Any=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=4096						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Union[str, Any]=0						,		_UpperCAmelCase					:						Any=320						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					hash_seed
								_lowerCAmelCase			:			int        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					attention_head_size
								_lowerCAmelCase			:			Dict        =					hidden_size
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			int        =					num_hashes
								_lowerCAmelCase			:			Union[str, Any]        =					len(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else num_buckets
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_attn_chunk_length
								_lowerCAmelCase			:			List[str]        =					local_attn_chunk_length
								_lowerCAmelCase			:			int        =					lsh_num_chunks_after
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_num_chunks_before
								_lowerCAmelCase			:			str        =					local_num_chunks_after
								_lowerCAmelCase			:			Dict        =					local_num_chunks_before
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					feed_forward_size
								_lowerCAmelCase			:			str        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					lsh_attention_probs_dropout_prob
								_lowerCAmelCase			:			Dict        =					local_attention_probs_dropout_prob
								_lowerCAmelCase			:			Tuple        =					max_position_embeddings
								_lowerCAmelCase			:			Any        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					layer_norm_eps
								_lowerCAmelCase			:			Dict        =					axial_pos_embds
								_lowerCAmelCase			:			Union[str, Any]        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					axial_norm_std
								_lowerCAmelCase			:			List[str]        =					chunk_size_lm_head
								_lowerCAmelCase			:			int        =					attn_layers
								_lowerCAmelCase			:			List[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[int]        =					classifier_dropout
								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_decoder=_UpperCAmelCase						,		tie_word_embeddings=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

