





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


_UpperCAmelCase       : List[Any]			     =       get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_UpperCAmelCase       : int			     =       get_tests_dir("""fixtures/vocab.json""")
_UpperCAmelCase       : Tuple			     =       get_tests_dir("""fixtures""")

class      lowerCAmelCase							(      unittest.TestCase    ):
 UpperCAmelCase__									=	["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
 def   A_			(   self						:   List[Any]			)    ->			Dict:
     lowerCamelCase__					:   List[Any]      				=	0
 def   A_			(   self						:   Any			)    ->			str:
     lowerCamelCase__					:   Dict      				=	AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'			)
     self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)
 def   A_			(   self						:   int			)    ->			List[Any]:
     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__					:   Optional[Any]      				=	WavaVecaConfig()
         lowerCamelCase__					:   Dict      				=	AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'			)

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

         lowerCamelCase__					:   Any      				=	AutoProcessor.from_pretrained(UpperCamelCase__			)

     self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)
 def   A_			(   self						:   str			)    ->			int:
     with tempfile.TemporaryDirectory() as tmpdirname:
         # copy relevant files
         copyfile(UpperCamelCase__    ,  os.path.join(UpperCamelCase__    ,  UpperCamelCase__			)			)
         copyfile(UpperCamelCase__    ,  os.path.join(UpperCamelCase__    ,  'vocab.json'			)			)

         lowerCamelCase__					:   List[Any]      				=	AutoProcessor.from_pretrained(UpperCamelCase__			)

     self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)
 def   A_			(   self						:   Tuple			)    ->			Optional[int]:
     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__					:   Dict      				=	WavaVecaFeatureExtractor()
         lowerCamelCase__					:   Union[str, Any]      				=	AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'			)

         lowerCamelCase__					:   Optional[int]      				=	WavaVecaProcessor(UpperCamelCase__    ,  UpperCamelCase__			)

         # save in new folder
         processor.save_pretrained(UpperCamelCase__			)

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

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

         lowerCamelCase__					:   Optional[int]      				=	AutoProcessor.from_pretrained(UpperCamelCase__			)

     self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)
 def   A_			(   self						:   List[str]			)    ->			Dict:
     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__					:   Optional[int]      				=	WavaVecaFeatureExtractor()
         lowerCamelCase__					:   List[Any]      				=	AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'			)

         lowerCamelCase__					:   str      				=	WavaVecaProcessor(UpperCamelCase__    ,  UpperCamelCase__			)

         # save in new folder
         processor.save_pretrained(UpperCamelCase__			)

         # drop `processor_class` in feature extractor
         with open(os.path.join(UpperCamelCase__    ,  UpperCamelCase__			)    ,  'r'			) as f:
             lowerCamelCase__					:   List[Any]      				=	json.load(UpperCamelCase__			)
             config_dict.pop('processor_class'			)

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

         lowerCamelCase__					:   List[Any]      				=	AutoProcessor.from_pretrained(UpperCamelCase__			)

     self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)
 def   A_			(   self						:   Dict			)    ->			Optional[int]:
     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__					:   Any      				=	WavaVecaConfig(processor_class='Wav2Vec2Processor'			)
         model_config.save_pretrained(UpperCamelCase__			)
         # copy relevant files
         copyfile(UpperCamelCase__    ,  os.path.join(UpperCamelCase__    ,  'vocab.json'			)			)
         # create emtpy sample processor
         with open(os.path.join(UpperCamelCase__    ,  UpperCamelCase__			)    ,  'w'			) as f:
             f.write('{}'			)

         lowerCamelCase__					:   Union[str, Any]      				=	AutoProcessor.from_pretrained(UpperCamelCase__			)

     self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)
 def   A_			(   self						:   Tuple			)    ->			List[Any]:
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(UpperCamelCase__			):
         lowerCamelCase__					:   Union[str, Any]      				=	AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'			)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(UpperCamelCase__			):
         lowerCamelCase__					:   str      				=	AutoProcessor.from_pretrained(
             'hf-internal-testing/test_dynamic_processor'    ,  trust_remote_code=UpperCamelCase__			)

     lowerCamelCase__					:   int      				=	AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'    ,  trust_remote_code=UpperCamelCase__			)
     self.assertTrue(processor.special_attribute_present			)
     self.assertEqual(processor.__class__.__name__    ,  'NewProcessor'			)

     lowerCamelCase__					:   List[Any]      				=	processor.feature_extractor
     self.assertTrue(feature_extractor.special_attribute_present			)
     self.assertEqual(feature_extractor.__class__.__name__    ,  'NewFeatureExtractor'			)

     lowerCamelCase__					:   List[Any]      				=	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
         lowerCamelCase__					:   Dict      				=	AutoProcessor.from_pretrained(
             'hf-internal-testing/test_dynamic_processor'    ,  trust_remote_code=UpperCamelCase__    ,  use_fast=UpperCamelCase__			)
         lowerCamelCase__					:   int      				=	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   A_			(   self						:   Optional[Any]			)    ->			Any:
     try:
         AutoConfig.register('custom'    ,  UpperCamelCase__			)
         AutoFeatureExtractor.register(UpperCamelCase__    ,  UpperCamelCase__			)
         AutoTokenizer.register(UpperCamelCase__    ,  slow_tokenizer_class=UpperCamelCase__			)
         AutoProcessor.register(UpperCamelCase__    ,  UpperCamelCase__			)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(UpperCamelCase__			):
             AutoProcessor.register(UpperCamelCase__    ,  UpperCamelCase__			)

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

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

         lowerCamelCase__					:   Optional[Any]      				=	CustomProcessor(UpperCamelCase__    ,  UpperCamelCase__			)

         with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(UpperCamelCase__			)
             lowerCamelCase__					:   Union[str, Any]      				=	AutoProcessor.from_pretrained(UpperCamelCase__			)
             self.assertIsInstance(UpperCamelCase__    ,  UpperCamelCase__			)

     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   A_			(   self						:   int			)    ->			Tuple:

     class      lowerCAmelCase							(      SCREAMING_SNAKE_CASE_    ):
      UpperCAmelCase__									=	False


     class      lowerCAmelCase							(      SCREAMING_SNAKE_CASE_    ):
      UpperCAmelCase__									=	False


     class      lowerCAmelCase							(      SCREAMING_SNAKE_CASE_    ):
      UpperCAmelCase__									=	"""AutoFeatureExtractor"""
      UpperCAmelCase__									=	"""AutoTokenizer"""
      UpperCAmelCase__									=	False

     try:
         AutoConfig.register('custom'    ,  UpperCamelCase__			)
         AutoFeatureExtractor.register(UpperCamelCase__    ,  UpperCamelCase__			)
         AutoTokenizer.register(UpperCamelCase__    ,  slow_tokenizer_class=UpperCamelCase__			)
         AutoProcessor.register(UpperCamelCase__    ,  UpperCamelCase__			)
         # If remote code is not set, the default is to use local classes.
         lowerCamelCase__					:   List[Any]      				=	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.
         lowerCamelCase__					:   Any      				=	AutoProcessor.from_pretrained(
             'hf-internal-testing/test_dynamic_processor'    ,  trust_remote_code=UpperCamelCase__			)
         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.
         lowerCamelCase__					:   Union[str, Any]      				=	AutoProcessor.from_pretrained(
             'hf-internal-testing/test_dynamic_processor'    ,  trust_remote_code=UpperCamelCase__			)
         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   A_			(   self						:   Union[str, Any]			)    ->			int:
     lowerCamelCase__					:   str      				=	AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'			)
     self.assertEqual(processor.__class__.__name__    ,  'BertTokenizerFast'			)







 def   A_			(   self						:   List[str]			)    ->			Any:
     lowerCamelCase__					:   Union[str, Any]      				=	AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'			)
     self.assertEqual(processor.__class__.__name__    ,  'ConvNextImageProcessor'			)


@is_staging_test
class      lowerCAmelCase							(      unittest.TestCase    ):
 UpperCAmelCase__									=	["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
 @classmethod
 def   A_			(   cls						:   Optional[Any]			)    ->			Optional[int]:
     lowerCamelCase__					:   List[str]      				=	TOKEN
     HfFolder.save_token(UpperCamelCase__			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]			)    ->			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   A_			(   self						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__					:   Optional[Any]      				=	WavaVecaProcessor.from_pretrained(UpperCamelCase__			)
     with tempfile.TemporaryDirectory() as tmp_dir:
         processor.save_pretrained(
             os.path.join(UpperCamelCase__    ,  'test-processor'			)    ,  push_to_hub=UpperCamelCase__    ,  use_auth_token=self._token			)

         lowerCamelCase__					:   List[Any]      				=	WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor"""			)
         for k, v in processor.feature_extractor.__dict__.items():
             self.assertEqual(UpperCamelCase__    ,  getattr(new_processor.feature_extractor    ,  UpperCamelCase__			)			)
         self.assertDictEqual(new_processor.tokenizer.get_vocab()    ,  processor.tokenizer.get_vocab()			)
 def   A_			(   self						:   int			)    ->			Dict:
     lowerCamelCase__					:   int      				=	WavaVecaProcessor.from_pretrained(UpperCamelCase__			)

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

         lowerCamelCase__					:   List[str]      				=	WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'			)
         for k, v in processor.feature_extractor.__dict__.items():
             self.assertEqual(UpperCamelCase__    ,  getattr(new_processor.feature_extractor    ,  UpperCamelCase__			)			)
         self.assertDictEqual(new_processor.tokenizer.get_vocab()    ,  processor.tokenizer.get_vocab()			)







 def   A_			(   self						:   Optional[int]			)    ->			Any:
     CustomFeatureExtractor.register_for_auto_class()
     CustomTokenizer.register_for_auto_class()
     CustomProcessor.register_for_auto_class()

     lowerCamelCase__					:   Optional[Any]      				=	CustomFeatureExtractor.from_pretrained(UpperCamelCase__			)

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

     lowerCamelCase__					:   List[Any]      				=	CustomProcessor(UpperCamelCase__    ,  UpperCamelCase__			)

     with tempfile.TemporaryDirectory() as tmp_dir:
         create_repo(F"""{USER}/test-dynamic-processor"""    ,  token=self._token			)
         lowerCamelCase__					:   Union[str, Any]      				=	Repository(UpperCamelCase__    ,  clone_from=F"""{USER}/test-dynamic-processor"""    ,  token=self._token			)
         processor.save_pretrained(UpperCamelCase__			)

         # 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(UpperCamelCase__    ,  'tokenizer_config.json'			)			) as f:
             lowerCamelCase__					:   Optional[int]      				=	json.load(UpperCamelCase__			)
         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(UpperCamelCase__    ,  'custom_feature_extraction.py'			)			)			)
         self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__    ,  'custom_tokenization.py'			)			)			)
         self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__    ,  'custom_processing.py'			)			)			)

         repo.push_to_hub()

     lowerCamelCase__					:   Tuple      				=	AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""    ,  trust_remote_code=UpperCamelCase__			)
     # 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'			)












from pathlib import Path

import fire


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Union[str, Any]      				=	Path(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	Path(_UpperCAmelCase				)
    dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
    for path in src_dir.iterdir():
        lowerCamelCase__					:   Tuple      				=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
        lowerCamelCase__					:   Optional[int]      				=	dest_dir.joinpath(path.name				)
        print(_UpperCAmelCase				)
        dest_path.open('w'				).write('\n'.join(_UpperCAmelCase				)				)


if __name__ == "__main__":
    fire.Fire(minify)





