
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

_lowerCamelCase					   =logging.getLogger(__name__)





@dataclass
class 							A__		:
      _UpperCAmelCase				:    Optional[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."""
              )
          }  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """Whether to pad all samples to `max_seq_length`. """
                  """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
              )
          }  ,   )
      _UpperCAmelCase				:    Optional[int]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of training examples to this """
                  """value if set."""
              )
          }  ,   )
      _UpperCAmelCase				:    Optional[int]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                  """value if set."""
              )
          }  ,   )
      _UpperCAmelCase				:    Optional[int]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of prediction examples to this """
                  """value if set."""
              )
          }  ,   )





@dataclass
class 							A__		:
      _UpperCAmelCase				:    str           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
      _UpperCAmelCase				:    str           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Train language if it is different from the evaluation language."""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,   )
      _UpperCAmelCase				:    Optional[bool]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}  ,   )
      _UpperCAmelCase				:    str           =			field(
          default="""main"""  ,   metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                  """with private models)."""
              )
          }  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}  ,   )





def     _a					(						):
 # 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		:	str							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("""run_xnli""",	lowerCamelCase							)

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",	datefmt="""%m/%d/%Y %H:%M:%S""",	handlers=[logging.StreamHandler(sys.stdout							)],	)

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 lowerCamelCase		:	Optional[Any]							=      training_args.get_process_log_level()
 logger.setLevel(lowerCamelCase							)
 datasets.utils.logging.set_verbosity(lowerCamelCase							)
 transformers.utils.logging.set_verbosity(lowerCamelCase							)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + F'''distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}'''							)
 logger.info(F'''Training/evaluation parameters {training_args}'''							)

 # Detecting last checkpoint.
 lowerCamelCase		:	Optional[Any]							=      None
 if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
  lowerCamelCase		:	List[Any]							=      get_last_checkpoint(training_args.output_dir							)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
   raise ValueError(
       F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       """Use --overwrite_output_dir to overcome."""							)
  elif last_checkpoint is not None:
   logger.info(
       F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""							)

    # Set seed before initializing model.
 set_seed(training_args.seed							)

 # In distributed training, the load_dataset function guarantees that only one local process can concurrently
 # download the dataset.
 # Downloading and loading xnli dataset from the hub.
 if training_args.do_train:
  if model_args.train_language is None:
   lowerCamelCase		:	Union[str, Any]							=      load_dataset(
       """xnli""",	model_args.language,	split="""train""",	cache_dir=model_args.cache_dir,	use_auth_token=True if model_args.use_auth_token else None,	)
  else:
   lowerCamelCase		:	List[str]							=      load_dataset(
       """xnli""",	model_args.train_language,	split="""train""",	cache_dir=model_args.cache_dir,	use_auth_token=True if model_args.use_auth_token else None,	)
  lowerCamelCase		:	Optional[Any]							=      train_dataset.features["""label"""].names

 if training_args.do_eval:
  lowerCamelCase		:	List[str]							=      load_dataset(
      """xnli""",	model_args.language,	split="""validation""",	cache_dir=model_args.cache_dir,	use_auth_token=True if model_args.use_auth_token else None,	)
  lowerCamelCase		:	int							=      eval_dataset.features["""label"""].names

 if training_args.do_predict:
  lowerCamelCase		:	List[str]							=      load_dataset(
      """xnli""",	model_args.language,	split="""test""",	cache_dir=model_args.cache_dir,	use_auth_token=True if model_args.use_auth_token else None,	)
  lowerCamelCase		:	Dict							=      predict_dataset.features["""label"""].names

 # Labels
 lowerCamelCase		:	Tuple							=      len(lowerCamelCase							)

 # Load pretrained model and tokenizer
 # In 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=lowerCamelCase,	idalabel={str(lowerCamelCase							): label for i, label in enumerate(lowerCamelCase							)},	labelaid={label: i for i, label in enumerate(lowerCamelCase							)},	finetuning_task="""xnli""",	cache_dir=model_args.cache_dir,	revision=model_args.model_revision,	use_auth_token=True if model_args.use_auth_token else None,	)
 lowerCamelCase		:	Dict							=      AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,	do_lower_case=model_args.do_lower_case,	cache_dir=model_args.cache_dir,	use_fast=model_args.use_fast_tokenizer,	revision=model_args.model_revision,	use_auth_token=True if model_args.use_auth_token else None,	)
 lowerCamelCase		:	int							=      AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path							),	config=lowerCamelCase,	cache_dir=model_args.cache_dir,	revision=model_args.model_revision,	use_auth_token=True if model_args.use_auth_token else None,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,	)

 # Preprocessing the datasets
 # Padding strategy
 if data_args.pad_to_max_length:
  lowerCamelCase		:	Optional[Any]							=      """max_length"""
 else:
  # We will pad later, dynamically at batch creation, to the max sequence length in each batch
  lowerCamelCase		:	Tuple							=      False

 def preprocess_function(lowerCamelCase							):
  # Tokenize the texts
  return tokenizer(
      examples["""premise"""],	examples["""hypothesis"""],	padding=lowerCamelCase,	max_length=data_args.max_seq_length,	truncation=lowerCamelCase,	)

 if training_args.do_train:
  if data_args.max_train_samples is not None:
   lowerCamelCase		:	int							=      min(len(lowerCamelCase							),	data_args.max_train_samples							)
   lowerCamelCase		:	List[Any]							=      train_dataset.select(range(lowerCamelCase							)							)
  with training_args.main_process_first(desc="""train dataset map pre-processing"""							):
   lowerCamelCase		:	List[Any]							=      train_dataset.map(
       lowerCamelCase,	batched=lowerCamelCase,	load_from_cache_file=not data_args.overwrite_cache,	desc="""Running tokenizer on train dataset""",	)
  # Log a few random samples from the training set:
  for index in random.sample(range(len(lowerCamelCase							)							),	3							):
   logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''							)

 if training_args.do_eval:
  if data_args.max_eval_samples is not None:
   lowerCamelCase		:	Union[str, Any]							=      min(len(lowerCamelCase							),	data_args.max_eval_samples							)
   lowerCamelCase		:	Union[str, Any]							=      eval_dataset.select(range(lowerCamelCase							)							)
  with training_args.main_process_first(desc="""validation dataset map pre-processing"""							):
   lowerCamelCase		:	Union[str, Any]							=      eval_dataset.map(
       lowerCamelCase,	batched=lowerCamelCase,	load_from_cache_file=not data_args.overwrite_cache,	desc="""Running tokenizer on validation dataset""",	)

 if training_args.do_predict:
  if data_args.max_predict_samples is not None:
   lowerCamelCase		:	int							=      min(len(lowerCamelCase							),	data_args.max_predict_samples							)
   lowerCamelCase		:	Any							=      predict_dataset.select(range(lowerCamelCase							)							)
  with training_args.main_process_first(desc="""prediction dataset map pre-processing"""							):
   lowerCamelCase		:	Union[str, Any]							=      predict_dataset.map(
       lowerCamelCase,	batched=lowerCamelCase,	load_from_cache_file=not data_args.overwrite_cache,	desc="""Running tokenizer on prediction dataset""",	)

    # Get the metric function
 lowerCamelCase		:	Union[str, Any]							=      evaluate.load("""xnli"""							)

 # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
 # predictions and label_ids field) and has to return a dictionary string to float.
 def compute_metrics(lowerCamelCase							):
  lowerCamelCase		:	Optional[Any]							=      p.predictions[0] if isinstance(p.predictions,	lowerCamelCase							) else p.predictions
  lowerCamelCase		:	Union[str, Any]							=      np.argmax(lowerCamelCase,	axis=1							)
  return metric.compute(predictions=lowerCamelCase,	references=p.label_ids							)

 # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
 if data_args.pad_to_max_length:
  lowerCamelCase		:	List[Any]							=      default_data_collator
 elif training_args.fpaa:
  lowerCamelCase		:	Dict							=      DataCollatorWithPadding(lowerCamelCase,	pad_to_multiple_of=8							)
 else:
  lowerCamelCase		:	List[str]							=      None

 # Initialize our Trainer
 lowerCamelCase		:	str							=      Trainer(
     model=lowerCamelCase,	args=lowerCamelCase,	train_dataset=train_dataset if training_args.do_train else None,	eval_dataset=eval_dataset if training_args.do_eval else None,	compute_metrics=lowerCamelCase,	tokenizer=lowerCamelCase,	data_collator=lowerCamelCase,	)

 # Training
 if training_args.do_train:
  lowerCamelCase		:	List[Any]							=      None
  if training_args.resume_from_checkpoint is not None:
   lowerCamelCase		:	List[Any]							=      training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   lowerCamelCase		:	int							=      last_checkpoint
  lowerCamelCase		:	Dict							=      trainer.train(resume_from_checkpoint=lowerCamelCase							)
  lowerCamelCase		:	Tuple							=      train_result.metrics
  lowerCamelCase		:	Union[str, Any]							=      (
      data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase							)
  )
  lowerCamelCase		:	Any							=      min(lowerCamelCase,	len(lowerCamelCase							)							)

  trainer.save_model()  # Saves the tokenizer too for easy upload

  trainer.log_metrics("""train""",	lowerCamelCase							)
  trainer.save_metrics("""train""",	lowerCamelCase							)
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""							)
  lowerCamelCase		:	str							=      trainer.evaluate(eval_dataset=lowerCamelCase							)

  lowerCamelCase		:	Optional[int]							=      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase							)
  lowerCamelCase		:	Any							=      min(lowerCamelCase,	len(lowerCamelCase							)							)

  trainer.log_metrics("""eval""",	lowerCamelCase							)
  trainer.save_metrics("""eval""",	lowerCamelCase							)

 # Prediction
 if training_args.do_predict:
  logger.info("""*** Predict ***"""							)
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      trainer.predict(lowerCamelCase,	metric_key_prefix="""predict"""							)

  lowerCamelCase		:	Optional[int]							=      (
      data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase							)
  )
  lowerCamelCase		:	Any							=      min(lowerCamelCase,	len(lowerCamelCase							)							)

  trainer.log_metrics("""predict""",	lowerCamelCase							)
  trainer.save_metrics("""predict""",	lowerCamelCase							)

  lowerCamelCase		:	int							=      np.argmax(lowerCamelCase,	axis=1							)
  lowerCamelCase		:	List[Any]							=      os.path.join(training_args.output_dir,	"""predictions.txt"""							)
  if trainer.is_world_process_zero():
   with open(lowerCamelCase,	"""w"""							) as writer:
    writer.write("""index\tprediction\n"""							)
    for index, item in enumerate(lowerCamelCase							):
     lowerCamelCase		:	Optional[int]							=      label_list[item]
     writer.write(F'''{index}\t{item}\n'''							)


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					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




