




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_keras_nlp_available():
  from transformers.models.gpta import TFGPTaTokenizer


_lowerCamelCase			:	Union[str, Any]							 =     ["""gpt2"""]
_lowerCamelCase			:	Any							 =     """gpt2"""

if is_tf_available():


  class        UpperCamelCase_					(				tf.Module  ):

      '''simple docstring'''






      def __init__(    self   :			str      ,				UpperCAmelCase__   :			str)		->Dict:





             '''simple docstring'''

             super().__init__()
             A__   				=					tokenizer
             A__   				=					AutoConfig.from_pretrained(UpperCAmelCase__)
             A__   				=					TFGPTaLMHeadModel.from_config(UpperCAmelCase__)





      @tf.function(input_signature=(tf.TensorSpec((None,)      ,				tf.string      ,				name='''text'''),))
      def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			Optional[int])		->List[Any]:





             '''simple docstring'''

             A__   				=					self.tokenizer(UpperCAmelCase__)
             A__   				=					tokenized['''input_ids'''].to_tensor()

             A__   				=					tf.cast(input_ids_dense > 0      ,				tf.intaa)
             # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

             A__   				=					self.model(input_ids=UpperCAmelCase__      ,				attention_mask=UpperCAmelCase__)['''logits''']

             return outputs


@require_tf
@require_keras_nlp
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           super().setUp()

           A__   				=					[GPTaTokenizer.from_pretrained(UpperCAmelCase__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
           A__   				=					[TFGPTaTokenizer.from_pretrained(UpperCAmelCase__) for checkpoint in TOKENIZER_CHECKPOINTS]
           assert len(self.tokenizers) == len(self.tf_tokenizers)

           A__   				=					[
               '''This is a straightforward English test sentence.''',
               '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
               '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
               '''And some much more rare Chinese: 齉 堃 齉堃''',
               '''Je vais aussi écrire en français pour tester les accents''',
               '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
           ]
           A__   				=					list(zip(self.test_sentences      ,				self.test_sentences[::-1]))


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Any:





           '''simple docstring'''

           for tokenizer, tf_tokenizer in zip(self.tokenizers      ,				self.tf_tokenizers):
                  for test_inputs in self.test_sentences:
                         A__   				=					tokenizer([test_inputs]      ,				return_tensors='''tf''')
                         A__   				=					tf_tokenizer([test_inputs])

                         for key in python_outputs.keys():
                                # convert them to numpy to avoid messing with ragged tensors
                                A__   				=					python_outputs[key].numpy()
                                A__   				=					tf_outputs[key].numpy()

                                self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
                                self.assertTrue(tf.reduce_all(tf.cast(UpperCAmelCase__      ,				tf.intaa) == tf_outputs_values))


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  A__   				=					tf.function(UpperCAmelCase__)
                  for test_inputs in self.test_sentences:
                         A__   				=					tf.constant(UpperCAmelCase__)
                         A__   				=					compiled_tokenizer(UpperCAmelCase__)
                         A__   				=					tf_tokenizer(UpperCAmelCase__)

                         for key in eager_outputs.keys():
                                self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->List[str]:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  A__   				=					ModelToSave(tokenizer=UpperCAmelCase__)
                  A__   				=					tf.convert_to_tensor([self.test_sentences[0]])
                  A__   				=					model.serving(UpperCAmelCase__)  # Build model with some sample inputs
                  with TemporaryDirectory() as tempdir:
                         A__   				=					Path(UpperCAmelCase__) / '''saved.model'''
                         tf.saved_model.save(UpperCAmelCase__      ,				UpperCAmelCase__      ,				signatures={'''serving_default''': model.serving})
                         A__   				=					tf.saved_model.load(UpperCAmelCase__)
                  A__   				=					loaded_model.signatures['''serving_default'''](UpperCAmelCase__)['''output_0''']
                  # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
                  self.assertTrue(tf.reduce_all(out == loaded_output))


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[Any]:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  A__   				=					tf.convert_to_tensor([self.test_sentences[0]])
                  A__   				=					tf_tokenizer(UpperCAmelCase__)  # Build model with some sample inputs

                  A__   				=					tf_tokenizer.get_config()
                  A__   				=					TFGPTaTokenizer.from_config(UpperCAmelCase__)
                  A__   				=					model_from_config(UpperCAmelCase__)

                  for key in from_config_output.keys():
                         self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->str:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  # for the test to run
                  A__   				=					123_123

                  for max_length in [3, 5, 1_024]:
                         A__   				=					tf.convert_to_tensor([self.test_sentences[0]])
                         A__   				=					tf_tokenizer(UpperCAmelCase__      ,				max_length=UpperCAmelCase__)

                         A__   				=					out['''input_ids'''].numpy().shape[1]

                         assert out_length == max_length








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			:	Any							 =     logging.getLogger(__name__)


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   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__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      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__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                '''value if set.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                '''value if set.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
                '''value if set.'''
            )
        }	,      )


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






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







def SCREAMING_SNAKE_CASE       ( )     -> str:



       """simple docstring"""

       A__   				=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
       A__				,							A__				,							A__   				=					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'''				,       lowercase_		)

       # 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()

       A__   				=					training_args.get_process_log_level()
       logger.setLevel(lowercase_		)
       datasets.utils.logging.set_verbosity(lowercase_		)
       transformers.utils.logging.set_verbosity(lowercase_		)
       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.
       A__   				=					None
       if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
              A__   				=					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:
                     A__   				=					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:
                     A__   				=					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				,       )
              A__   				=					train_dataset.features['''label'''].names

       if training_args.do_eval:
              A__   				=					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				,       )
              A__   				=					eval_dataset.features['''label'''].names

       if training_args.do_predict:
              A__   				=					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				,       )
              A__   				=					predict_dataset.features['''label'''].names

       # Labels
       A__   				=					len(lowercase_		)

       # Load pretrained model and tokenizer
       # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       A__   				=					AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       num_labels=lowercase_				,       idalabel={str(lowercase_		): label for i, label in enumerate(lowercase_		)}				,       labelaid={label: i for i, label in enumerate(lowercase_		)}				,       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				,       )
       A__   				=					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				,       )
       A__   				=					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				,       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:
              A__   				=					'''max_length'''
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              A__   				=					False

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

       if training_args.do_train:
              if data_args.max_train_samples is not None:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_train_samples		)
                     A__   				=					train_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''train dataset map pre-processing'''		):
                     A__   				=					train_dataset.map(
                         lowercase_				,       batched=lowercase_				,       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(lowercase_		)		)				,       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:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_eval_samples		)
                     A__   				=					eval_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''validation dataset map pre-processing'''		):
                     A__   				=					eval_dataset.map(
                         lowercase_				,       batched=lowercase_				,       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:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_predict_samples		)
                     A__   				=					predict_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''prediction dataset map pre-processing'''		):
                     A__   				=					predict_dataset.map(
                         lowercase_				,       batched=lowercase_				,       load_from_cache_file=not data_args.overwrite_cache				,       desc='''Running tokenizer on prediction dataset'''				,       )

    # Get the metric function
       A__   				=					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(lowercase_		):
              A__   				=					p.predictions[0] if isinstance(p.predictions				,       lowercase_		) else p.predictions
              A__   				=					np.argmax(lowercase_				,       axis=1		)
              return metric.compute(predictions=lowercase_				,       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:
              A__   				=					default_data_collator
       elif training_args.fpaa:
              A__   				=					DataCollatorWithPadding(lowercase_				,       pad_to_multiple_of=8		)
       else:
              A__   				=					None

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

       # Training
       if training_args.do_train:
              A__   				=					None
              if training_args.resume_from_checkpoint is not None:
                     A__   				=					training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     A__   				=					last_checkpoint
              A__   				=					trainer.train(resume_from_checkpoint=lowercase_		)
              A__   				=					train_result.metrics
              A__   				=					(
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_		)
              )
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

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

              trainer.log_metrics('''train'''				,       lowercase_		)
              trainer.save_metrics('''train'''				,       lowercase_		)
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''		)
              A__   				=					trainer.evaluate(eval_dataset=lowercase_		)

              A__   				=					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_		)
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

              trainer.log_metrics('''eval'''				,       lowercase_		)
              trainer.save_metrics('''eval'''				,       lowercase_		)

       # Prediction
       if training_args.do_predict:
              logger.info('''*** Predict ***'''		)
              A__				,							A__				,							A__   				=					trainer.predict(lowercase_				,       metric_key_prefix='''predict'''		)

              A__   				=					(
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase_		)
              )
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

              trainer.log_metrics('''predict'''				,       lowercase_		)
              trainer.save_metrics('''predict'''				,       lowercase_		)

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


if __name__ == "__main__":
  main()


