




"""simple docstring"""



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


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




class      UpperCAmelCase_  (						unittest.TestCase							):
    lowercase__						 =				['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




    def 							__magic_name__							(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     A__	=    0




    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     A__	=    AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"				)
     self.assertIsInstance(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Union[str, Any]:




     '''simple docstring'''

     with tempfile.TemporaryDirectory() as tmpdirname:
      A__	=    WavaVecaConfig()
      A__	=    AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"				)

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

      A__	=    AutoProcessor.from_pretrained(snake_case_				)

     self.assertIsInstance(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							Dict:




     '''simple docstring'''

     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"				)				)

      A__	=    AutoProcessor.from_pretrained(snake_case_				)

     self.assertIsInstance(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							int:




     '''simple docstring'''

     with tempfile.TemporaryDirectory() as tmpdirname:
      A__	=    WavaVecaFeatureExtractor()
      A__	=    AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"				)

      A__	=    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:
       A__	=    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_				)				)

      A__	=    AutoProcessor.from_pretrained(snake_case_				)

     self.assertIsInstance(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	str				)				->							Optional[Any]:




     '''simple docstring'''

     with tempfile.TemporaryDirectory() as tmpdirname:
      A__	=    WavaVecaFeatureExtractor()
      A__	=    AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"				)

      A__	=    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:
       A__	=    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_				)				)

      A__	=    AutoProcessor.from_pretrained(snake_case_				)

     self.assertIsInstance(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     with tempfile.TemporaryDirectory() as tmpdirname:
      A__	=    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("{}"				)

      A__	=    AutoProcessor.from_pretrained(snake_case_				)

     self.assertIsInstance(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	str				)				->							Tuple:




     '''simple docstring'''

     with self.assertRaises(snake_case_				):
      A__	=    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_				):
      A__	=    AutoProcessor.from_pretrained(
          "hf-internal-testing/test_dynamic_processor"     ,			trust_remote_code=snake_case_				)

     A__	=    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"				)

     A__	=    processor.feature_extractor
     self.assertTrue(feature_extractor.special_attribute_present				)
     self.assertEqual(feature_extractor.__class__.__name__     ,			"NewFeatureExtractor"				)

     A__	=    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
      A__	=    AutoProcessor.from_pretrained(
          "hf-internal-testing/test_dynamic_processor"     ,			trust_remote_code=snake_case_     ,			use_fast=snake_case_				)
      A__	=    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 							__magic_name__							(   self						:	List[str]				)				->							Any:




     '''simple docstring'''

     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
      A__	=    CustomFeatureExtractor.from_pretrained(snake_case_				)

      with tempfile.TemporaryDirectory() as tmp_dir:
       A__	=    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]				)				)
       A__	=    CustomTokenizer(snake_case_				)

      A__	=    CustomProcessor(snake_case_     ,			snake_case_				)

      with tempfile.TemporaryDirectory() as tmp_dir:
       processor.save_pretrained(snake_case_				)
       A__	=    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 							__magic_name__							(   self						:	int				)				->							Dict:




     '''simple docstring'''

     class      UpperCAmelCase_  (						A_							):
         lowercase__						 =				False






     class      UpperCAmelCase_  (						A_							):
         lowercase__						 =				False






     class      UpperCAmelCase_  (						A_							):
         lowercase__						 =				'''AutoFeatureExtractor'''
         lowercase__						 =				'''AutoTokenizer'''
         lowercase__						 =				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.
      A__	=    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.
      A__	=    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.
      A__	=    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 							__magic_name__							(   self						:	Any				)				->							List[str]:




     '''simple docstring'''

     A__	=    AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"				)
     self.assertEqual(processor.__class__.__name__     ,			"BertTokenizerFast"				)




    def 							__magic_name__							(   self						:	Any				)				->							Dict:




     '''simple docstring'''

     A__	=    AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"				)
     self.assertEqual(processor.__class__.__name__     ,			"ConvNextImageProcessor"				)






@is_staging_test
class      UpperCAmelCase_  (						unittest.TestCase							):
    lowercase__						 =				['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




    @classmethod
    def 							__magic_name__							(   cls						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    TOKEN
     HfFolder.save_token(snake_case_				)




    @classmethod
    def 							__magic_name__							(   cls						:	str				)				->							Tuple:




     '''simple docstring'''

     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 							__magic_name__							(   self						:	Any				)				->							List[str]:




     '''simple docstring'''

     A__	=    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				)

      A__	=    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 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    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"     ,			)

      A__	=    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 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     CustomFeatureExtractor.register_for_auto_class()
     CustomTokenizer.register_for_auto_class()
     CustomProcessor.register_for_auto_class()

     A__	=    CustomFeatureExtractor.from_pretrained(snake_case_				)

     with tempfile.TemporaryDirectory() as tmp_dir:
      A__	=    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]				)				)
      A__	=    CustomTokenizer(snake_case_				)

     A__	=    CustomProcessor(snake_case_     ,			snake_case_				)

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

     A__	=    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"				)











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





