




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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = None					,     )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						{}

    if train_file is not None:
        __UpperCamelCase  		=						[train_file]
    if eval_file is not None:
        __UpperCamelCase  		=						[eval_file]
    if test_file is not None:
        __UpperCamelCase  		=						[test_file]

    __UpperCamelCase  		=						datasets.load_dataset('''csv'''					,     data_files=__lowerCAmelCase      )
    __UpperCamelCase  		=						list(ds[list(files.keys()      )[0]].features.keys()      )
    __UpperCamelCase  		=						features_name.pop(__lowerCAmelCase      )
    __UpperCamelCase  		=						list(set(ds[list(files.keys()      )[0]][label_name]      )      )
    __UpperCamelCase  		=						{label: i for i, label in enumerate(__lowerCAmelCase      )}
    __UpperCamelCase  		=						tokenizer.model_input_names
    __UpperCamelCase  		=						{}

    if len(__lowerCAmelCase      ) == 1:
        for k in files.keys():
            __UpperCamelCase  		=						ds[k].map(
                lambda lowercase_      : tokenizer.batch_encode_plus(
                    example[features_name[0]]					,     truncation=__lowerCAmelCase					,     max_length=__lowerCAmelCase					,     padding='''max_length'''      )					,     batched=__lowerCAmelCase					,     )
    elif len(__lowerCAmelCase      ) == 2:
        for k in files.keys():
            __UpperCamelCase  		=						ds[k].map(
                lambda lowercase_      : tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])					,     truncation=__lowerCAmelCase					,     max_length=__lowerCAmelCase					,     padding='''max_length'''					,     )					,     batched=__lowerCAmelCase					,     )

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            __UpperCamelCase  		=						{k: v for k, v in ex.items() if k in input_names}
            __UpperCamelCase  		=						labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            __UpperCamelCase  		=						{k: v for k, v in ex.items() if k in input_names}
            __UpperCamelCase  		=						labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            __UpperCamelCase  		=						{k: v for k, v in ex.items() if k in input_names}
            __UpperCamelCase  		=						labelaid[ex[label_name]]
            yield (d, label)

    __UpperCamelCase  		=						(
        tf.data.Dataset.from_generator(
            __lowerCAmelCase					,     ({k: tf.intaa for k in input_names}, tf.intaa)					,     ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))					,     )
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        __UpperCamelCase  		=						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

    __UpperCamelCase  		=						(
        tf.data.Dataset.from_generator(
            __lowerCAmelCase					,     ({k: tf.intaa for k in input_names}, tf.intaa)					,     ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))					,     )
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        __UpperCamelCase  		=						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

    __UpperCamelCase  		=						(
        tf.data.Dataset.from_generator(
            __lowerCAmelCase					,     ({k: tf.intaa for k in input_names}, tf.intaa)					,     ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))					,     )
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        __UpperCamelCase  		=						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

    return train_ds, val_ds, test_ds, labelaid


a_   		=     logging.getLogger(__name__)

@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					field(metadata={"help": "Which column contains the label"}   )
      lowerCAmelCase__			: Optional[Any]    =					field(default=__UpperCAmelCase							,			metadata={"help": "The path of the training file"}   )
      lowerCAmelCase__			: Optional[Any]    =					field(default=__UpperCAmelCase							,			metadata={"help": "The path of the development file"}   )
      lowerCAmelCase__			: Optional[int]    =					field(default=__UpperCAmelCase							,			metadata={"help": "The path of the test file"}   )
      lowerCAmelCase__			: Optional[Any]    =					field(
          default=1_28							,			metadata={
              "help": (
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }							,			)
      lowerCAmelCase__			: List[str]    =					field(
          default=__UpperCAmelCase							,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )







@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
      lowerCAmelCase__			: Union[str, Any]    =					field(
          default=__UpperCAmelCase							,			metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
      lowerCAmelCase__			: Dict    =					field(
          default=__UpperCAmelCase							,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
      lowerCAmelCase__			: Optional[int]    =					field(default=__UpperCAmelCase							,			metadata={"help": "Set this flag to use fast tokenization."}   )
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      lowerCAmelCase__			: Optional[int]    =					field(
          default=__UpperCAmelCase							,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,			)







def 					__SCREAMING_SNAKE_CASE   (  )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						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					,     )
    logger.info(
        F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1      )}, "
        F"16-bits training: {training_args.fpaa}"      )
    logger.info(F"Training/evaluation parameters {training_args}"      )

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

    __UpperCamelCase  		=						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		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						get_tfds(
        train_file=data_args.train_file					,     eval_file=data_args.dev_file					,     test_file=data_args.test_file					,     tokenizer=__lowerCAmelCase					,     label_column_id=data_args.label_column_id					,     max_seq_length=data_args.max_seq_length					,     )

    __UpperCamelCase  		=						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,     num_labels=len(__lowerCAmelCase      )					,     labelaid=__lowerCAmelCase					,     idalabel={id: label for label, id in labelaid.items()}					,     finetuning_task='''text-classification'''					,     cache_dir=model_args.cache_dir					,     )

    with training_args.strategy.scope():
        __UpperCamelCase  		=						TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path					,     from_pt=bool('''.bin''' in model_args.model_name_or_path      )					,     config=__lowerCAmelCase					,     cache_dir=model_args.cache_dir					,     )

    def compute_metrics(lowercase_      ) -> Dict:
        __UpperCamelCase  		=						np.argmax(p.predictions					,     axis=1      )

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    __UpperCamelCase  		=						TFTrainer(
        model=__lowerCAmelCase					,     args=__lowerCAmelCase					,     train_dataset=__lowerCAmelCase					,     eval_dataset=__lowerCAmelCase					,     compute_metrics=__lowerCAmelCase					,     )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
    __UpperCamelCase  		=						{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''      )
        __UpperCamelCase  		=						trainer.evaluate()
        __UpperCamelCase  		=						os.path.join(training_args.output_dir					,     '''eval_results.txt'''      )

        with open(__lowerCAmelCase					,     '''w'''      ) as writer:
            logger.info('''***** Eval results *****'''      )

            for key, value in result.items():
                logger.info(F"  {key} = {value}"      )
                writer.write(F"{key} = {value}\n"      )

            results.update(__lowerCAmelCase      )

    return results


if __name__ == "__main__":
 main()












import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


a_   		=     get_tests_dir("fixtures/dummy_feature_extractor_config.json")
a_   		=     get_tests_dir("fixtures/vocab.json")
a_   		=     get_tests_dir("fixtures")

class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						0







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)
          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig()
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              # save in new folder
              model_config.save_pretrained(snake_case				)
              processor.save_pretrained(snake_case				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Any				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       snake_case				)				)
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

              # drop `processor_class` in tokenizer
              with open(os.path.join(snake_case ,       snake_case				) ,       '''r'''				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
                  config_dict.pop('''processor_class'''				)

              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write(json.dumps(snake_case				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Tuple				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

              # drop `processor_class` in feature extractor
              with open(os.path.join(snake_case ,       snake_case				) ,       '''r'''				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
                  config_dict.pop('''processor_class'''				)

              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write(json.dumps(snake_case				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Dict				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig(processor_class='''Wav2Vec2Processor'''				)
              model_config.save_pretrained(snake_case				)
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)
              # create emtpy sample processor
              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write('''{}'''				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: int				):
          # If remote code is not set, we will time out when asking whether to load the model.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)

          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
          self.assertTrue(processor.special_attribute_present				)
          self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)

          __UpperCamelCase  		=						processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present				)
          self.assertEqual(feature_extractor.__class__.__name__ ,       '''NewFeatureExtractor'''				)

          __UpperCamelCase  		=						processor.tokenizer
          self.assertTrue(tokenizer.special_attribute_present				)
          if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__ ,       '''NewTokenizerFast'''				)

              # Test we can also load the slow version
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case ,       use_fast=snake_case				)
              __UpperCamelCase  		=						new_processor.tokenizer
              self.assertTrue(new_tokenizer.special_attribute_present				)
              self.assertEqual(new_tokenizer.__class__.__name__ ,       '''NewTokenizer'''				)
          else:
              self.assertEqual(tokenizer.__class__.__name__ ,       '''NewTokenizer'''				)







      def       snake_case	(     self					: List[Any]				):
          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(snake_case				):
                  AutoProcessor.register(snake_case ,       snake_case				)

              # Now that the config is registered, it can be used as any other config with the auto-API
              __UpperCamelCase  		=						CustomFeatureExtractor.from_pretrained(snake_case				)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  __UpperCamelCase  		=						os.path.join(snake_case ,       '''vocab.txt'''				)
                  with open(snake_case ,       '''w''' ,       encoding='''utf-8'''				) as vocab_writer:
                      vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]				)				)
                  __UpperCamelCase  		=						CustomTokenizer(snake_case				)

              __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(snake_case				)
                  __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)
                  self.assertIsInstance(snake_case ,       snake_case				)

          finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                  del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
              if CustomConfig in PROCESSOR_MAPPING._extra_content:
                  del PROCESSOR_MAPPING._extra_content[CustomConfig]







      def       snake_case	(     self					: Optional[int]				):

          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Any    =					"AutoFeatureExtractor"
                lowerCAmelCase__			: str    =					"AutoTokenizer"
                lowerCAmelCase__			: str    =					False

          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # If remote code is not set, the default is to use local classes.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
              self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)
              self.assertFalse(processor.special_attribute_present				)
              self.assertFalse(processor.feature_extractor.special_attribute_present				)
              self.assertFalse(processor.tokenizer.special_attribute_present				)

              # If remote code is disabled, we load the local ones.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
              self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)
              self.assertFalse(processor.special_attribute_present				)
              self.assertFalse(processor.feature_extractor.special_attribute_present				)
              self.assertFalse(processor.tokenizer.special_attribute_present				)

              # If remote is enabled, we load from the Hub.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
              self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)
              self.assertTrue(processor.special_attribute_present				)
              self.assertTrue(processor.feature_extractor.special_attribute_present				)
              self.assertTrue(processor.tokenizer.special_attribute_present				)

          finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                  del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
              if CustomConfig in PROCESSOR_MAPPING._extra_content:
                  del PROCESSOR_MAPPING._extra_content[CustomConfig]







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''BertTokenizerFast'''				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''ConvNextImageProcessor'''				)







@is_staging_test
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      @classmethod
      def       snake_case	(     cls					: List[str]				):
          __UpperCamelCase  		=						TOKEN
          HfFolder.save_token(snake_case				)







      @classmethod
      def       snake_case	(     cls					: Union[str, Any]				):
          try:
              delete_repo(token=cls._token ,       repo_id='''test-processor'''				)
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token ,       repo_id='''valid_org/test-processor-org'''				)
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token ,       repo_id='''test-dynamic-processor'''				)
          except HTTPError:
              pass







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)
          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(snake_case ,       '''test-processor'''				) ,       push_to_hub=snake_case ,       use_auth_token=self._token				)

              __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"				)
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(snake_case ,       getattr(new_processor.feature_extractor ,       snake_case				)				)
              self.assertDictEqual(new_processor.tokenizer.get_vocab() ,       processor.tokenizer.get_vocab()				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(snake_case ,       '''test-processor-org'''				) ,       push_to_hub=snake_case ,       use_auth_token=self._token ,       organization='''valid_org''' ,       )

              __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''				)
              for k, v in processor.feature_extractor.__dict__.items():
                  self.assertEqual(snake_case ,       getattr(new_processor.feature_extractor ,       snake_case				)				)
              self.assertDictEqual(new_processor.tokenizer.get_vocab() ,       processor.tokenizer.get_vocab()				)







      def       snake_case	(     self					: List[str]				):
          CustomFeatureExtractor.register_for_auto_class()
          CustomTokenizer.register_for_auto_class()
          CustomProcessor.register_for_auto_class()

          __UpperCamelCase  		=						CustomFeatureExtractor.from_pretrained(snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              __UpperCamelCase  		=						os.path.join(snake_case ,       '''vocab.txt'''				)
              with open(snake_case ,       '''w''' ,       encoding='''utf-8'''				) as vocab_writer:
                  vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]				)				)
              __UpperCamelCase  		=						CustomTokenizer(snake_case				)

          __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(F"{USER}/test-dynamic-processor" ,       token=self._token				)
              __UpperCamelCase  		=						Repository(snake_case ,       clone_from=F"{USER}/test-dynamic-processor" ,       token=self._token				)
              processor.save_pretrained(snake_case				)

              # This has added the proper auto_map field to the feature extractor config
              self.assertDictEqual(
                  processor.feature_extractor.auto_map ,       {
                      '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
                      '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                  } ,       )

              # This has added the proper auto_map field to the tokenizer config
              with open(os.path.join(snake_case ,       '''tokenizer_config.json'''				)				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
              self.assertDictEqual(
                  tokenizer_config['''auto_map'''] ,       {
                      '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                      '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                  } ,       )

              # The code has been copied from fixtures
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_feature_extraction.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_tokenization.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_processing.py'''				)				)				)

              repo.push_to_hub()

          __UpperCamelCase  		=						AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor" ,       trust_remote_code=snake_case				)
          # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
          self.assertEqual(new_processor.__class__.__name__ ,       '''CustomProcessor'''				)






