

"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A							=  get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  XGLMTokenizer
  SCREAMING_SNAKE_CASE					      =  XGLMTokenizerFast
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  True



  def  _a      (self	):


        """simple docstring"""

        super().setUp()

        # We have a SentencePiece fixture for testing
        UpperCAmelCase__			:							Optional[int]							=    XGLMTokenizer(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    """<pad>"""
        UpperCAmelCase__			:							List[Any]							=    1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase	)							,       _lowerCamelCase	)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase	)							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    list(self.get_tokenizer().get_vocab().keys()	)

        self.assertEqual(vocab_keys[0]							,       """<s>"""	)
        self.assertEqual(vocab_keys[1]							,       """<pad>"""	)
        self.assertEqual(len(_lowerCamelCase	)							,       1008	)



  def  _a      (self	):


        """simple docstring"""

        self.assertEqual(self.get_tokenizer().vocab_size							,       1008	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    XGLMTokenizer(_lowerCamelCase							,       keep_accents=_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    tokenizer.tokenize("""This is a test"""	)
        self.assertListEqual(_lowerCamelCase							,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_lowerCamelCase	)							,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,       )

        UpperCAmelCase__			:							Dict							=    tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """9""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """é""",
                """.""",
            ]							,       )
        UpperCAmelCase__			:							Optional[Any]							=    tokenizer.convert_tokens_to_ids(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]							,       )

        UpperCAmelCase__			:							Optional[int]							=    tokenizer.convert_ids_to_tokens(_lowerCamelCase	)
        self.assertListEqual(
            _lowerCamelCase							,       [
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """<unk>""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """<unk>""",
                """.""",
            ]							,       )



  @cached_property
  def  _a      (self	):


        """simple docstring"""

        return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""	)



  def  _a      (self	):


        """simple docstring"""

        with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(_lowerCamelCase							,       f.name	)
              UpperCAmelCase__			:							Optional[Any]							=    XGLMTokenizer(f.name							,       keep_accents=_lowerCamelCase	)
              UpperCAmelCase__			:							int							=    pickle.dumps(_lowerCamelCase	)
        pickle.loads(_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        if not self.test_rust_tokenizer:
              return

        UpperCAmelCase__			:							List[Any]							=    self.get_tokenizer()
        UpperCAmelCase__			:							Optional[int]							=    self.get_rust_tokenizer()

        UpperCAmelCase__			:							Dict							=    """I was born in 92000, and this is falsé."""

        UpperCAmelCase__			:							Any							=    tokenizer.tokenize(_lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    rust_tokenizer.tokenize(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Any							=    tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    rust_tokenizer.encode(_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Tuple							=    self.get_rust_tokenizer()
        UpperCAmelCase__			:							Optional[Any]							=    tokenizer.encode(_lowerCamelCase	)
        UpperCAmelCase__			:							List[str]							=    rust_tokenizer.encode(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    """Hello World!"""
        UpperCAmelCase__			:							Dict							=    [2, 31227, 4447, 35]

        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)



  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    (
            """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
            """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
        )
        # fmt: off
        UpperCAmelCase__			:							Union[str, Any]							=    [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
        # fmt: on

        self.assertListEqual(_lowerCamelCase							,       self.big_tokenizer.encode(_lowerCamelCase	)	)




  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    {
            """input_ids""": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
            """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
        }  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_lowerCamelCase							,       model_name="""facebook/xglm-564M"""							,       padding=_lowerCamelCase							,       )




"""simple docstring"""
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


_A							=  logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  def __init__(self							,       *_lowerCamelCase							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       **_lowerCamelCase	):


        """simple docstring"""

        super().__init__(*_lowerCamelCase							,       **_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    eval_examples
        UpperCAmelCase__			:							Union[str, Any]							=    post_process_function
        UpperCAmelCase__			:							int							=    quant_trainer_args
        UpperCAmelCase__			:							Dict							=    128  # default number of calibration samples



  def  _a      (self							,       _lowerCamelCase=None	):


        """simple docstring"""

        if calib_dataset is None and self.calib_dataset is None:
              raise ValueError("""Trainer: calibration requires an calib_dataset."""	)
        UpperCAmelCase__			:							List[str]							=    calib_dataset if calib_dataset is not None else self.calib_dataset

        UpperCAmelCase__			:							Optional[int]							=    self._remove_unused_columns(_lowerCamelCase							,       description="""Calibration"""	)

        return DataLoader(
            _lowerCamelCase							,       batch_size=self.args.eval_batch_size							,       collate_fn=self.data_collator							,       drop_last=self.args.dataloader_drop_last							,       num_workers=self.args.dataloader_num_workers							,       pin_memory=self.args.dataloader_pin_memory							,       shuffle=_lowerCamelCase							,       )



  def  _a      (self							,       _lowerCamelCase=None	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self.train_dataset if calib_dataset is None else calib_dataset
        UpperCAmelCase__			:							List[str]							=    self.get_calib_dataloader(_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    self.model
        quant_trainer.configure_model(_lowerCamelCase							,       self.quant_trainer_args							,       calib=_lowerCamelCase	)
        model.eval()
        quant_trainer.enable_calibration(_lowerCamelCase	)

        logger.info("""***** Running calibration *****"""	)
        logger.info(F"""  Num examples = {self.calib_num}"""	)
        logger.info(F"""  Batch size = {calib_dataloader.batch_size}"""	)

        for step, inputs in enumerate(_lowerCamelCase	):
              # Prediction step
              UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.prediction_step(_lowerCamelCase							,       _lowerCamelCase							,       prediction_loss_only=_lowerCamelCase	)
              if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                    break

        quant_trainer.finish_calibration(_lowerCamelCase							,       self.quant_trainer_args	)
        UpperCAmelCase__			:							Tuple							=    model



  def  _a      (self							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       _lowerCamelCase = "eval"	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self.eval_dataset if eval_dataset is None else eval_dataset
        UpperCAmelCase__			:							List[Any]							=    self.get_eval_dataloader(_lowerCamelCase	)
        UpperCAmelCase__			:							Tuple							=    self.eval_examples if eval_examples is None else eval_examples

        # Temporarily disable metric computation, we will do it in the loop here.
        UpperCAmelCase__			:							Optional[Any]							=    self.compute_metrics
        UpperCAmelCase__			:							int							=    None
        UpperCAmelCase__			:							int							=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
              UpperCAmelCase__			:							str							=    eval_loop(
                  _lowerCamelCase							,       description="""Evaluation"""							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=_lowerCamelCase							,       )
        finally:
              UpperCAmelCase__			:							List[str]							=    compute_metrics

        if self.post_process_function is not None and self.compute_metrics is not None:
              UpperCAmelCase__			:							int							=    self.post_process_function(_lowerCamelCase							,       _lowerCamelCase							,       output.predictions	)
              UpperCAmelCase__			:							List[Any]							=    self.compute_metrics(_lowerCamelCase	)

              # Prefix all keys with metric_key_prefix + '_'
              for key in list(metrics.keys()	):
                    if not key.startswith(F"""{metric_key_prefix}_"""	):
                          UpperCAmelCase__			:							Optional[int]							=    metrics.pop(_lowerCamelCase	)

              self.log(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							List[Any]							=    {}

        if self.args.tpu_metrics_debug or self.args.debug:
              # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
              xm.master_print(met.metrics_report()	)

        UpperCAmelCase__			:							Optional[int]							=    self.callback_handler.on_evaluate(self.args							,       self.state							,       self.control							,       _lowerCamelCase	)
        return metrics



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase=None							,       _lowerCamelCase = "test"	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    self.get_test_dataloader(_lowerCamelCase	)

        # Temporarily disable metric computation, we will do it in the loop here.
        UpperCAmelCase__			:							Union[str, Any]							=    self.compute_metrics
        UpperCAmelCase__			:							Optional[Any]							=    None
        UpperCAmelCase__			:							Any							=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
              UpperCAmelCase__			:							Optional[Any]							=    eval_loop(
                  _lowerCamelCase							,       description="""Prediction"""							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=_lowerCamelCase							,       )
        finally:
              UpperCAmelCase__			:							Dict							=    compute_metrics

        if self.post_process_function is None or self.compute_metrics is None:
              return output

        UpperCAmelCase__			:							Dict							=    self.post_process_function(_lowerCamelCase							,       _lowerCamelCase							,       output.predictions							,       """predict"""	)
        UpperCAmelCase__			:							Any							=    self.compute_metrics(_lowerCamelCase	)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()	):
              if not key.startswith(F"""{metric_key_prefix}_"""	):
                    UpperCAmelCase__			:							Any							=    metrics.pop(_lowerCamelCase	)

        return PredictionOutput(predictions=predictions.predictions							,       label_ids=predictions.label_ids							,       metrics=_lowerCamelCase	)




  def  _a      (self							,       _lowerCamelCase="./"	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self.eval_dataset
        UpperCAmelCase__			:							int							=    self.get_eval_dataloader(_lowerCamelCase	)

        UpperCAmelCase__			:							Union[str, Any]							=    next(iter(_lowerCamelCase	)	)

        # saving device - to make it consistent
        UpperCAmelCase__			:							int							=    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)

        # convert to tuple
        UpperCAmelCase__			:							List[Any]							=    tuple(v.to(_lowerCamelCase	) for k, v in batch.items()	)

        logger.info("""Converting model to be onnx compatible"""	)
        from pytorch_quantization.nn import TensorQuantizer

        UpperCAmelCase__			:							Optional[Any]							=    True

        UpperCAmelCase__			:							Tuple							=    self.model.to(_lowerCamelCase	)

        model.eval()
        model.float()

        UpperCAmelCase__			:							str							=    model.module if hasattr(_lowerCamelCase							,       """module"""	) else model
        quant_trainer.configure_model(_lowerCamelCase							,       self.quant_trainer_args	)

        UpperCAmelCase__			:							List[str]							=    os.path.join(_lowerCamelCase							,       """model.onnx"""	)
        logger.info(F"""exporting model to {output_model_file}"""	)

        UpperCAmelCase__			:							Optional[Any]							=    {0: """batch_size""", 1: """seq_len"""}

        torch.onnx.export(
            _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       export_params=_lowerCamelCase							,       opset_version=13							,       do_constant_folding=_lowerCamelCase							,       input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]							,       output_names=["""output_start_logits""", """output_end_logits"""]							,       dynamic_axes={
                """input_ids""": axes,
                """attention_mask""": axes,
                """token_type_ids""": axes,
                """output_start_logits""": axes,
                """output_end_logits""": axes,
            }							,       verbose=_lowerCamelCase							,       )
        logger.info("""onnx export finished"""	)

