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


UpperCAmelCase      		=					logging.getLogger(__name__)




@dataclass
class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
 _lowerCamelCase    :						Optional[str]					     =			field(
     default=a_,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
 _lowerCamelCase    :						Optional[str]					     =			field(
     default=a_,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
 _lowerCamelCase    :						Optional[str]					     =			field(
     default=a_,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},						)







@dataclass
class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			field(
     metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()    )}    )
 _lowerCamelCase    :						str					     =			field(
     metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
 _lowerCamelCase    :						int					     =			field(
     default=128,						metadata={
         'help': (
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     },						)
 _lowerCamelCase    :						bool					     =			field(
     default=a_,						metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







def SCREAMING_SNAKE_CASE					(  lowercase_:						List[InputFeatures]					):
       _UpperCamelCase  :							Union[str, Any]	    =	default_data_collator(lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	batch.pop('pairID'		,    lowercase_					)
       return batch


def SCREAMING_SNAKE_CASE					(  ):
       # 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.

       _UpperCamelCase  :							Tuple	    =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	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'		,    lowercase_					)

       # Set seed
       set_seed(training_args.seed					)

       try:
              _UpperCamelCase  :							str	    =	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.

       _UpperCamelCase  :							List[Any]	    =	AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path		,    num_labels=lowercase_		,    finetuning_task=data_args.task_name		,    cache_dir=model_args.cache_dir		,    )
       _UpperCamelCase  :							List[str]	    =	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		,    )
       _UpperCamelCase  :							List[str]	    =	AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path		,    from_tf=bool('.ckpt' in model_args.model_name_or_path					)		,    config=lowercase_		,    cache_dir=model_args.cache_dir		,    )

       # Get datasets
       _UpperCamelCase  :							Any	    =	(
           HansDataset(
               data_dir=data_args.data_dir		,    tokenizer=lowercase_		,    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
       )
       _UpperCamelCase  :							Optional[int]	    =	(
           HansDataset(
               data_dir=data_args.data_dir		,    tokenizer=lowercase_		,    task=data_args.task_name		,    max_seq_length=data_args.max_seq_length		,    overwrite_cache=data_args.overwrite_cache		,    evaluate=lowercase_		,    )
           if training_args.do_eval
           else None
       )

       # Initialize our Trainer
       _UpperCamelCase  :							Dict	    =	Trainer(
           model=lowercase_		,    args=lowercase_		,    train_dataset=lowercase_		,    eval_dataset=lowercase_		,    data_collator=lowercase_		,    )

       # 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 ***'					)

              _UpperCamelCase  :							List[Any]	    =	trainer.predict(lowercase_					)
              _UpperCamelCase  :							Any	    =	output.predictions
              _UpperCamelCase  :							Optional[int]	    =	np.argmax(lowercase_		,    axis=1					)

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

              trainer._log(output.metrics					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Dict					):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Union[str, Any]						 =      'blenderbot'
      a		:List[str]						 =      ['past_key_values']
      a		:Any						 =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__( self					,					_UpperCAmelCase=8_008					,					_UpperCAmelCase=128					,					_UpperCAmelCase=2					,					_UpperCAmelCase=10_240					,					_UpperCAmelCase=32					,					_UpperCAmelCase=24					,					_UpperCAmelCase=10_240					,					_UpperCAmelCase=32					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=2_560					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=1					,					_UpperCAmelCase=False					,					_UpperCAmelCase=0					,					_UpperCAmelCase=1					,					_UpperCAmelCase=2					,					_UpperCAmelCase=3					,					_UpperCAmelCase=2					,					**_UpperCAmelCase					,					):
            lowerCAmelCase_						=      vocab_size
            lowerCAmelCase_						=      max_position_embeddings
            lowerCAmelCase_						=      d_model
            lowerCAmelCase_						=      encoder_ffn_dim
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      encoder_attention_heads
            lowerCAmelCase_						=      decoder_ffn_dim
            lowerCAmelCase_						=      decoder_layers
            lowerCAmelCase_						=      decoder_attention_heads
            lowerCAmelCase_						=      dropout
            lowerCAmelCase_						=      attention_dropout
            lowerCAmelCase_						=      activation_dropout
            lowerCAmelCase_						=      activation_function
            lowerCAmelCase_						=      init_std
            lowerCAmelCase_						=      encoder_layerdrop
            lowerCAmelCase_						=      decoder_layerdrop
            lowerCAmelCase_						=      use_cache
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=_UpperCAmelCase					,					bos_token_id=_UpperCAmelCase					,					eos_token_id=_UpperCAmelCase					,					is_encoder_decoder=_UpperCAmelCase					,					decoder_start_token_id=_UpperCAmelCase					,					encoder_no_repeat_ngram_size=_UpperCAmelCase					,					forced_eos_token_id=_UpperCAmelCase					,					**_UpperCAmelCase					,					)


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      @property
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_						=      {0: '''batch'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                  else:
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}
                  if self.use_past:
                        self.fill_with_past_key_values_(_UpperCAmelCase					,					direction='''inputs''')
            elif self.task == "causal-lm":
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            else:
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                          ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                      ])

            return common_inputs







      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super().outputs
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self).outputs
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            return common_outputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            # Generate decoder inputs
            lowerCAmelCase_						=      seq_length if not self.use_past else 1
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowerCAmelCase_						=      dict(**_UpperCAmelCase					,					**_UpperCAmelCase)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                        import torch
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      common_inputs['''decoder_input_ids'''].shape[1]
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowerCAmelCase_						=      decoder_seq_length
                  lowerCAmelCase_						=      (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )
                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      []
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers

                  for _ in range(_UpperCAmelCase):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                            ))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                        import torch
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      seqlen
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowerCAmelCase_						=      common_inputs['''attention_mask'''].dtype
                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase					,					dtype=_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      [
                      (torch.zeros(_UpperCAmelCase), torch.zeros(_UpperCAmelCase)) for _ in range(_UpperCAmelCase)
                  ]
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            # Copied from OnnxConfig.generate_dummy_inputs
            # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_						=      compute_effective_axis_dimension(
                _UpperCAmelCase					,					fixed_dimension=OnnxConfig.default_fixed_batch					,					num_token_to_add=0)

            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            lowerCAmelCase_						=      tokenizer.num_special_tokens_to_add(_UpperCAmelCase)
            lowerCAmelCase_						=      compute_effective_axis_dimension(
                _UpperCAmelCase					,					fixed_dimension=OnnxConfig.default_fixed_sequence					,					num_token_to_add=_UpperCAmelCase)

            # Generate dummy inputs according to compute batch and sequence
            lowerCAmelCase_						=      [''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
            lowerCAmelCase_						=      dict(tokenizer(_UpperCAmelCase					,					return_tensors=_UpperCAmelCase))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)

            elif self.task == "causal-lm":
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_causal_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)

            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super()._flatten_past_key_values_(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self)._flatten_past_key_values_(
                      _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if direction not in ["inputs", "outputs"]:
                  raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

            lowerCAmelCase_						=      '''past_key_values''' if direction == '''inputs''' else '''present'''
            lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers

            lowerCAmelCase_						=      '''past_encoder_sequence'''
            lowerCAmelCase_						=      '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

            for i in range(_UpperCAmelCase):
                  lowerCAmelCase_						=      {0: '''batch''', 2: decoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: decoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: encoder_sequence}
                  lowerCAmelCase_						=      {0: '''batch''', 2: encoder_sequence}



