
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


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






class     UpperCAmelCase						(		unittest.TestCase					):
   A__   :						List[Any]			=	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Tuple     							= 0



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"					)
          self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Union[str, Any]:


          '''simple docstring'''


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

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

                 snake_case			:  str     							= AutoProcessor.from_pretrained(__lowerCAmelCase					)

          self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
                 # copy relevant files
                 copyfile(__lowerCAmelCase       ,		os.path.join(__lowerCAmelCase       ,		__lowerCAmelCase					)					)
                 copyfile(__lowerCAmelCase       ,		os.path.join(__lowerCAmelCase       ,		"vocab.json"					)					)

                 snake_case			:  Union[str, Any]     							= AutoProcessor.from_pretrained(__lowerCAmelCase					)

          self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Dict:


          '''simple docstring'''


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

                 snake_case			:  Optional[Any]     							= WavaVecaProcessor(__lowerCAmelCase       ,		__lowerCAmelCase					)

                 # save in new folder
                 processor.save_pretrained(__lowerCAmelCase					)

                 # drop `processor_class` in tokenizer
                 with open(os.path.join(__lowerCAmelCase       ,		__lowerCAmelCase					)       ,		"r"					) as f:
                        snake_case			:  List[str]     							= json.load(__lowerCAmelCase					)
                        config_dict.pop("processor_class"					)

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

                 snake_case			:  List[str]     							= AutoProcessor.from_pretrained(__lowerCAmelCase					)

          self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase					)



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


          '''simple docstring'''


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

                 snake_case			:  Union[str, Any]     							= WavaVecaProcessor(__lowerCAmelCase       ,		__lowerCAmelCase					)

                 # save in new folder
                 processor.save_pretrained(__lowerCAmelCase					)

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

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

                 snake_case			:  Union[str, Any]     							= AutoProcessor.from_pretrained(__lowerCAmelCase					)

          self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase					)



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


          '''simple docstring'''


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

                 snake_case			:  Optional[Any]     							= AutoProcessor.from_pretrained(__lowerCAmelCase					)

          self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase					)



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


          '''simple docstring'''


          with self.assertRaises(__lowerCAmelCase					):
                 snake_case			:  Any     							= AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"					)
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(__lowerCAmelCase					):
                 snake_case			:  Union[str, Any]     							= AutoProcessor.from_pretrained(
                     "hf-internal-testing/test_dynamic_processor"       ,		trust_remote_code=__lowerCAmelCase					)

          snake_case			:  str     							= AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"       ,		trust_remote_code=__lowerCAmelCase					)
          self.assertTrue(processor.special_attribute_present					)
          self.assertEqual(processor.__class__.__name__       ,		"NewProcessor"					)

          snake_case			:  Union[str, Any]     							= processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present					)
          self.assertEqual(feature_extractor.__class__.__name__       ,		"NewFeatureExtractor"					)

          snake_case			:  Tuple     							= 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
                 snake_case			:  int     							= AutoProcessor.from_pretrained(
                     "hf-internal-testing/test_dynamic_processor"       ,		trust_remote_code=__lowerCAmelCase       ,		use_fast=__lowerCAmelCase					)
                 snake_case			:  Dict     							= 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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Any:


          '''simple docstring'''


          try:
                 AutoConfig.register("custom"       ,		__lowerCAmelCase					)
                 AutoFeatureExtractor.register(__lowerCAmelCase       ,		__lowerCAmelCase					)
                 AutoTokenizer.register(__lowerCAmelCase       ,		slow_tokenizer_class=__lowerCAmelCase					)
                 AutoProcessor.register(__lowerCAmelCase       ,		__lowerCAmelCase					)
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(__lowerCAmelCase					):
                        AutoProcessor.register(__lowerCAmelCase       ,		__lowerCAmelCase					)

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

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

                 snake_case			:  Any     							= CustomProcessor(__lowerCAmelCase       ,		__lowerCAmelCase					)

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

          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 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      List[str]:


          '''simple docstring'''


          class     UpperCAmelCase						(		A_					):
             A__   :						Dict			=	False






          class     UpperCAmelCase						(		A_					):
             A__   :						Union[str, Any]			=	False






          class     UpperCAmelCase						(		A_					):
             A__   :						Union[str, Any]			=	'''AutoFeatureExtractor'''
             A__   :						List[Any]			=	'''AutoTokenizer'''
             A__   :						Optional[int]			=	False

          try:
                 AutoConfig.register("custom"       ,		__lowerCAmelCase					)
                 AutoFeatureExtractor.register(__lowerCAmelCase       ,		__lowerCAmelCase					)
                 AutoTokenizer.register(__lowerCAmelCase       ,		slow_tokenizer_class=__lowerCAmelCase					)
                 AutoProcessor.register(__lowerCAmelCase       ,		__lowerCAmelCase					)
                 # If remote code is not set, the default is to use local classes.
                 snake_case			:  int     							= 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.
                 snake_case			:  str     							= AutoProcessor.from_pretrained(
                     "hf-internal-testing/test_dynamic_processor"       ,		trust_remote_code=__lowerCAmelCase					)
                 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.
                 snake_case			:  Tuple     							= AutoProcessor.from_pretrained(
                     "hf-internal-testing/test_dynamic_processor"       ,		trust_remote_code=__lowerCAmelCase					)
                 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 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Tuple:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Any:


          '''simple docstring'''


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






@is_staging_test
class     UpperCAmelCase						(		unittest.TestCase					):
   A__   :						Union[str, Any]			=	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']



   @classmethod
   def 							_SCREAMING_SNAKE_CASE  (cls						:   List[Any]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Dict     							= TOKEN
          HfFolder.save_token(__lowerCAmelCase					)



   @classmethod
   def 							_SCREAMING_SNAKE_CASE  (cls						:   str					)						->      int:


          '''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 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Any     							= WavaVecaProcessor.from_pretrained(__lowerCAmelCase					)
          with tempfile.TemporaryDirectory() as tmp_dir:
                 processor.save_pretrained(
                     os.path.join(__lowerCAmelCase       ,		"test-processor"					)       ,		push_to_hub=__lowerCAmelCase       ,		use_auth_token=self._token					)

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Any:


          '''simple docstring'''


          snake_case			:  Any     							= WavaVecaProcessor.from_pretrained(__lowerCAmelCase					)

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

                 snake_case			:  Optional[Any]     							= WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"					)
                 for k, v in processor.feature_extractor.__dict__.items():
                        self.assertEqual(__lowerCAmelCase       ,		getattr(new_processor.feature_extractor       ,		__lowerCAmelCase					)					)
                 self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,		processor.tokenizer.get_vocab()					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[Any]:


          '''simple docstring'''


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

          snake_case			:  Union[str, Any]     							= CustomFeatureExtractor.from_pretrained(__lowerCAmelCase					)

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

          snake_case			:  Tuple     							= CustomProcessor(__lowerCAmelCase       ,		__lowerCAmelCase					)

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

                 # 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(__lowerCAmelCase       ,		"tokenizer_config.json"					)					) as f:
                        snake_case			:  Union[str, Any]     							= json.load(__lowerCAmelCase					)
                 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(__lowerCAmelCase       ,		"custom_feature_extraction.py"					)					)					)
                 self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase       ,		"custom_tokenization.py"					)					)					)
                 self.assertTrue(os.path.isfile(os.path.join(__lowerCAmelCase       ,		"custom_processing.py"					)					)					)

                 repo.push_to_hub()

          snake_case			:  List[Any]     							= AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""       ,		trust_remote_code=__lowerCAmelCase					)
          # 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"					)












def   __lowerCamelCase   (				__a  :str       )						->							bool:






		"""simple docstring"""

		A__     =  0
		for ch in input_str:
				A__     =  ord(__a       )
				A__     =  pow(2       ,	__a       )

				# If we already turned on bit for current character's unicode
				if bitmap >> ch_unicode & 1 == 1:
						return False
				bitmap |= ch_bit_index_on
		return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()



